Convolution Kernel Size Research Articles

A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation

Background and Objective:Breast cancer is the most common cancer type among women worldwide and a leading cause of female death. Accurately interpreting these complex tumors, involving small size and morphology, requires a significant amount of expertise and time. Developing a breast tumor segmentation model to assist clinicians in treatment, therefore, holds great practical significance. Methods:We propose a multi-scale, multi-task model framework named MTF-UNet. Firstly, we differ from the common approach of using different convolution kernel sizes to extract multi-scale features, and instead use the same convolution kernel size with different numbers of convolutions to obtain multi-scale, multi-level features. Additionally, to better integrate features from different levels and sizes, we extract a new multi-branch feature fusion block (ADF). This block differs from using channel and spatial attention to fuse features, but considers fusion weights between various branches. Secondly, we propose to use the number of pixels predicted to be related to tumors and background to assist segmentation, which is different from the conventional approach of using classification tasks to assist segmentation. Results:We conducted extensive experiments on our proprietary DCE-MRI dataset, as well as two public datasets (BUSI and Kvasir-SEG). In the aforementioned datasets, our model achieved excellent MIoU scores of 90.4516%, 89.8408%, and 92.8431% on the respective test sets. Furthermore, our ablation study has demonstrated the efficacy of each component and the effective integration of our auxiliary prediction branch into other models. Conclusion:Through comprehensive experiments and comparisons with other algorithms, the effectiveness, adaptability, and robustness of our proposed method have been demonstrated. We believe that MTF-UNet has great potential for further development in the field of medical image segmentation. The relevant code and data can be found at https://github.com/LCUDai/MTF-UNet.git.

Numerical investigation of the effective receptive field and its relationship with convolutional kernels and layers in convolutional neural network

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.

Rolling bearing fault diagnosis method based on adaptive signal diagnosis network and its application

Aiming at the problems of fixed convolution kernel size and poor targeting of extracted features in the application of deep learning in fault diagnosis, this paper develops a fault diagnosis framework called adaptive signal diagnosis network (ASDN), in which the EEMD of multi-scale signal decomposition is improved in the adaptive preprocessing stage to adaptively capture the transient changes of signals. Meanwhile, SSA is improved to further extract fault trends and periodic components to optimize the signal representation. In the adaptive deep learning stage, an innovative temporal convolutional network (TCN) with a dynamic adjustment mechanism was developed to enable the neural network to adjust its convolutional kernel size according to different frequency components so as to accurately process signals of different frequencies. Validation on datasets from Case Western Reserve University and Xi'an Jiaotong University shows the superior performance of the proposed method, with diagnostic accuracies of 100% and 97.42% on these two public datasets, respectively.

Multi-Source Information-Based Bearing Fault Diagnosis Using Multi-Branch Selective Fusion Deep Residual Network.

Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model.

Layout Congestion Prediction Based on Regression-ViT

To accelerate the back-end design flow of integrated circuit (IC), numerous studies have made exploratory advancements in machine learning (ML) for electronic design automation (EDA). However, most research works are limited to deep learning (DL) models predominantly based on convolutional neural networks, and the models often suffer from poor generalization due to the scarcity of data. In this study, we propose the Double generative adversarial networks (D-GAN) model to enrich the dataset and propose the Regression Vision Transformer (R-ViT) model to predict layout congestion information. Compared to the baseline model, experimental results show improvements of 3.03% and 2.64% in Receiver Operating Characteristic-Area under Curve (ROC-AUC) and Precision-Recall Curve-Area under Curve (PRC-AUC) respectively. To further enhance the prediction accuracy of the model, an adaptive Huber loss function is designed to optimize the training process, resulting in an improvement of up to 11.03% in ROC-AUC compared with the baseline model. Lastly, extended experiments are conducted to study the effects of parameters and convolutional kernel size on performance, which find a better configuration.

Impact Load Localization Based on Multi-Scale Feature Fusion Convolutional Neural Network.

In order to achieve impact load localization of complex structures such as ships, this paper proposes a multi-scale feature fusion convolutional neural network (MSFF-CNN) method for impact load localization. An end-to-end machine learning model is used, where the raw vibration signals of impact loads are directly fed into the network model to avoid the process of feature extraction. Automatic feature learning and feature concatenation of the signal are achieved through four independent convolutional layers, each using a different size of convolutional kernel. Data normalization and L2 regularization techniques are introduced to enhance the data and prevent overfitting. Classification and localization of impact loads are accomplished using a softmax classification layer. Validation experiments are carried out using a ship's stern compartment model. Our results show that the classification and localization accuracy of the impact load sample group of MSFF-CNN reaches 94.29% compared with a traditional CNN. The method further improves the ability of the network to extract state features, takes local perception and global vision into account, effectively improves the classification ability of the model, and has good prospects for engineering applications.

G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.

Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction, limited contextual information and restricted feature extraction scales during feature extraction. This paper proposed a LLMR method based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet). The denoised sEMG time series was transformed into Gramian Angular Difference Field (GADF) matrix based on GAF. The MS-ResNet model, incorporating ResNet and multiscale feature fusion concepts, was proposed to comprehensively capture global and local information through different-scale feature extraction and fusion, so as to improve recognition performance. sEMG signals from 11 muscles of the preferred leg of 15 healthy subjects were recorded during six common lower limb movements. Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance. The study revealed that selecting k as 13, coupled with 11 muscles, yielded optimal model performance with the average cross-individual recognition accuracy reaching 97.62 %, demonstrating the model’s efficiency in LLMR. This method could provide a viable solution for developing more efficient and reliable LLMR systems, applicable to lower limb exoskeleton robots and intelligent prosthetics.

Phase unwrapping of SAR interferogram from modified U-net via training data simulation and network structure optimization

Phase unwrapping is the process of retrieving the true phase values from observed wrapped phases by adding the correct multiples of 2π. This process is crucial in synthetic aperture radar (SAR) interferometry, and numerous studies have aimed to enhance its performance. This study explored phase unwrapping using a modified U-Net regression model by optimizing both the network structure and training data. For network structure optimization, the study first compared model performance based on the size ratio of the lowest feature maps, determined by the number of pooling layers, to the size of convolutional kernels. A multi-kernel U-Net structure was developed to ensure robustness against variations in phase noise and gradient, compared to a standard single-kernel U-Net. Regarding the training data, data augmentation was implemented to address imbalances and better represent the local noise characteristics found in actual SAR interferograms. The training data was simulated to include local noise effects based on coherence measurements from real SAR data, as well as simple noise used for benchmarking the unwrapping performance with different training datasets. The results indicated that when the convolutional kernel size is smaller than the feature map size at the lowest layer, increasing the number of pooling layers leads to improvements in unwrapping performance. Conversely, performance decreased when the feature map size at the lowest layers was smaller than the convolutional kernel size. Specifically, the single-kernel U-Net with six pooling layers and the multi-kernel U-Net with five pooling layers exhibited the best unwrapping performance. Considering both simulated and real synthetic aperture radar interferogram data, the mean absolute errors for the single- and multi-kernel U-Net trained with simple noise were approximately 0.235 and 0.254, respectively. In contrast, for models trained with locally variable noise simulation data, the MAEs dropped to about 0.033 and 0.032, showing an improvement by approximately eightfold over models trained with simple noise. For real synthetic aperture radar interferograms, the mean absolute errors were 0.542 (single-kernel U-Net trained using simple noise), 0.592 (multi-kernel U-Net trained using simple noise), 0.542 (single-kernel U-Net trained using local noise), and 0.445 (proposed), respectively, underscoring the significant impact of training data on unwrapping performance. The study also evaluated the performance of the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU), obtaining mean absolute errors of about 0.043 for simulation data and 0.861 for real SAR data. Consequently, the multi-kernel model trained with locally different noise simulation data demonstrated roughly twice the performance compared to the traditional phase unwrapping method.

In-situ monitoring method of femtosecond laser welding between glass and copper with acoustic emission

The use of femtosecond laser welding has created new possibilities for connecting glasses and metals, revolutionizing the fabrication and assembly of advanced electronic components. However, monitoring the connection process during femtosecond laser welding is challenging due to the small heat-affected zone and brief interaction time. This study utilizes acoustic emission to monitor femtosecond laser welding and introduces the “Sum of Relative RMS” feature to correlate connection formation with acoustic emission signals in glass-copper welding. The mechanism and signal occurrence trends during welding are explained through in-situ signal monitoring and offline scanning electron microscopy characterization. Additionally, a Convolutional Neural Network is enhanced by increasing the convolutional kernel size, integrating multi-dimensional features, and incorporating prior knowledge via a customized loss function, resulting in an increase in the accuracy of successful welding detection from 89% to 95%. This research provides new insights into the monitoring of femtosecond laser glass-metal heterogeneous welding.

An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

ETCNN: An ensemble transformer-convolutional neural network for automatic analysis of fetal heart rate

ObjectiveTraditional methods face challenges in accurately analyzing fetal heart rate (FHR) signals due to the complexity of accelerations and decelerations (Acc/Dec) and their cyclic definition relationship with baseline. We aim to develop a deep learning model, Ensemble Transformer-Convolutional Neural Network (ETCNN), to improve baseline/Acc/Dec determination accuracy and validate its generalization across multi-center and multi-device test datasets. MethodsWe proposed ETCNN as a solution, treating FHR analysis as a one-dimensional signal segmentation problem. ETCNN consists of four subnetworks (TCNNs), each equipped with convolutional kernel size of 21, 31, 61, and 81, respectively. Each subnetwork integrates Channel-Residual (C-Res) modules and Channel Cross fusion with Transformer (CCT) modules. C-Res modules dynamically prune irrelevant channels, focusing on critical FHR episodes, while CCT modules harness multi-scale features to narrow semantic gaps. ResultsTrained on Lille Catholic University’s open-access database (LCU-DB), ETCNN’s performance surpassed twelve traditional methods and three deep learning models across four independent multi-center and multi-device test datasets. Ablation experiments demonstrated the effectiveness of ensemble learning, multi-scale convolution, residual channel attention, channel cross fusion attention, and multi-head attention in improving performance. Conclusion and significanceETCNN shows promise for accurate and efficient FHR analysis, with successful generalization across various datasets. Its advancements hold potential for clinical applications in fetal monitoring.

Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI.

Lumbar disc herniation is one of the most prevalent orthopedic issues in clinical practice. The lumbar spine is a crucial joint for movement and weight-bearing, so back pain can significantly impact the everyday lives of patients and is prone to recurring. The pathogenesis of lumbar disc herniation is complex and diverse, making it difficult to identify and assess after it has occurred. Magnetic resonance imaging (MRI) is the most effective method for detecting injuries, requiring continuous examination by medical experts to determine the extent of the injury. However, the continuous examination process is time-consuming and susceptible to errors. This study proposes an enhanced model, BE-YOLOv5, for hierarchical detection of lumbar disc herniation from MRI images. To tailor the training of the model to the job requirements, a specialized dataset was created. The data was cleaned and improved before the final calibration. A final training set of 2083 data points and a test set of 100 data points were obtained. The YOLOv5 model was enhanced by integrating the attention mechanism module, ECAnet, with a 3 × 3 convolutional kernel size, substituting its feature extraction network with a BiFPN, and implementing structural system pruning. The model achieved an 89.7% mean average precision (mAP) and 48.7 frames per second (FPS) on the test set. In comparison to Faster R-CNN, original YOLOv5, and the latest YOLOv8, this model performs better in terms of both accuracy and speed for the detection and grading of lumbar disc herniation from MRI, validating the effectiveness of multiple enhancement methods. The proposed model is expected to be used for diagnosing lumbar disc herniation from MRI images and to demonstrate efficient and high-precision performance.

Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging.

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model’s adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments.

Application of deep learning classification model for regional evaluation of roof pressure support evolution effects over time in coal mining face

Hydraulic support leg pressure serves as a crucial indicator for assessing work face support quality. Current evaluation methods for support quality primarily concentrate on static analyses-like inadequate initial support force, pressure overrun, and uneven bracket force-while neglecting dynamic column pressure changes. This paper introduces a model for assessing hydraulic support quality using deep learning techniques. Real-time data is preprocessed into a spatio-temporal pressure sub-matrix sample, which is then inputted into the model. This process assesses the support quality type and characterizes its dynamic evolution within the area. The model facilitates the identification of dynamic support quality effects in the working face area, aiding operators in making targeted adjustments to hydraulic support status. Experimental results revealed that the optimized LeNet-5 network-adjusting parameters like convolutional layer count, kernel size, and ReLU activation function-achieved the highest classification accuracy of 85.25 % for support quality, surpassed other networks. Furthermore, the improved LeNet-5 network outperformed other networks in both F1 score and recall. Additionally, the improved LeNet-5 network achieved faster convergence to the optimal solution, accelerated training speed. This highlighted its advantages in evaluating the spatio-temporal support quality of hydraulic supports in smart mining operations.

Expanding the defect image dataset of composite material coating with enhanced image-to-image translation

In the defect detection based on machine vision, the limited datasets have consistently impeded the efficacy of deep learning in defect identification. Restricted datasets lead to inadequate model generalization and reduced detection accuracy. Creating simulated images is one proposed solution to address this challenge. However, existing defect image generation methods have limitations in diversity and realism. An enhanced Image-to-image translation method, named E-Pix2Pix, is proposed for enhancing defect image datasets of composite material coating on solid motors to expand the current dataset and enhance the precision of classification models. This method not only improves the quality of generated images, but also has strong generalization ability. This study examines how different convolution kernel sizes and the coefficient of the L1 loss function affect the generation performance. The results of the experiments show that the defect images generated by E-Pix2Pix have realistic visual effects and better capture the essential features of defects. The structure similarity index measurement (SSIM) value reaches approximately 0.9 under appropriate parameters. Moreover, this study intends to investigate how the content of generated images impacts the classification model. By doubling the dataset size using the generated images, the accuracy of ResNet50 model recognition can be improved by 7%. The accuracy of ResNet101 model recognition can be improved by 5% when the dataset is expanded to 1.5 times. The generated images have harmful effects on the weaker models. Our method has also been validated on two public datasets, and satisfactory results have been obtained.

An attention mechanism module with spatial perception and channel information interaction

In the field of deep learning, the attention mechanism, as a technology that mimics human perception and attention processes, has made remarkable achievements. The current methods combine a channel attention mechanism and a spatial attention mechanism in a parallel or cascaded manner to enhance the model representational competence, but they do not fully consider the interaction between spatial and channel information. This paper proposes a method in which a space embedded channel module and a channel embedded space module are cascaded to enhance the model’s representational competence. First, in the space embedded channel module, to enhance the representational competence of the region of interest in different spatial dimensions, the input tensor is split into horizontal and vertical branches according to spatial dimensions to alleviate the loss of position information when performing 2D pooling. To smoothly process the features and highlight the local features, four branches are obtained through global maximum and average pooling, and the features are aggregated by different pooling methods to obtain two feature tensors with different pooling methods. To enable the output horizontal and vertical feature tensors to focus on different pooling features simultaneously, the two feature tensors are segmented and dimensionally transposed according to spatial dimensions, and the features are later aggregated along the spatial direction. Then, in the channel embedded space module, for the problem of no cross-channel connection between groups in grouped convolution and for which the parameters are large, this paper uses adaptive grouped banded matrices. Based on the banded matrices utilizing the mapping relationship that exists between the number of channels and the size of the convolution kernels, the convolution kernel size is adaptively computed to achieve adaptive cross-channel interaction, enhancing the correlation between the channel dimensions while ensuring that the spatial dimensions remain unchanged. Finally, the output horizontal and vertical weights are used as attention weights. In the experiment, the attention mechanism module proposed in this paper is embedded into the MobileNetV2 and ResNet networks at different depths, and extensive experiments are conducted on the CIFAR-10, CIFAR-100 and STL-10 datasets. The results show that the method in this paper captures and utilizes the features of the input data more effectively than the other methods, significantly improving the classification accuracy. Despite the introduction of an additional computational burden (0.5 M), however, the overall performance of the model still achieves the best results when the computational overhead is comprehensively considered.

A small target detection algorithm based on improved YOLOv5 in aerial image.

Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network's focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.