Inception Module Research Articles

Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction

Abstract Accurately predicting the remaining useful life of aerospace engines is crucial for enhancing the reliability of aviation equipment. While some methods have taken note of the challenges posed by vast sensor data and complex signal interrelationships, there is still room for improvement in performance. This paper proposes a novel deep learning model that utilizes a parallel structure to independently process inputs from various sensor signals. Each branch in this parallel structure employs a combination of an improved Inception module and a novel feature filtering module as a feature extractor. The improved Inception module boasts a larger perceptual field to ensure the integrity of feature information. The feature filtering module calculates the importance weights of feature information through convenient computation, allowing the network to focus more on feature information without significantly increasing computational complexity. Finally, the feature extractor is combined with a gated recurrent unit module to learn features from sensor signals. Extensive experiments were conducted on the C-MAPSS standard dataset, comparing the proposed method with other state-of-the-art methods. Ablation experiments were performed on the new generation N-CMAPSS standard dataset. The results of the experiments confirm the superiority and rationality of the proposed prediction method.

TCN-Inception: Temporal Convolutional Network and Inception modules for sensor-based Human Activity Recognition

The field of Human Activity Recognition (HAR) has experienced a significant surge in interest due to its essential role across numerous areas, including human–computer interaction (HCI), healthcare, smart homes, and various Internet of Things (IoT) applications. The power of deep learning methods in performing various classification tasks, including HAR, has been well-demonstrated. In light of this, our paper presents an efficient HAR system developed using a unique deep-learning architecture called TCN-Inception, which is designed for multivariate time series tasks like HAR data, by combining Temporal Convolutional Network (TCN) and Inception modules. The network starts with an Inception module that uses parallel convolution layers with various kernel sizes for feature extraction. It then includes a TCN module with dilated convolutions to grasp extended temporal patterns. Features are merged from different channels, and the use of residual connections and batch normalization improves training and deepens the architecture. We use four public datasets, UCI-HAR, MobiAct, Daphnet, and DSADS to assess the performance of the TCN-Inception model, and it obtains an average accuracy of 96.15%, 98.86%, 92.63%, and 99.50% for each dataset, respectively. Moreover, we compare the TCN-Inception to several deep learning frameworks to verify its performance. Finally, we implement an ablation study using several architectural configurations of the TCN-Inception model.

Radar Signal Recognition Based on CWD Spectrogram and Inception Module

Radar Signal Recognition Based on CWD Spectrogram and Inception Module

Capsule Network Based on Double-layer Attention Mechanism and Multi-scale Feature Extraction for Remaining Life Prediction

The era of big data provides a platform for high-precision RUL prediction, but the existing RUL prediction methods, which effectively extract key degradation information, remain a challenge. Existing methods ignore the influence of sensor and degradation moment variability, and instead assign weights to them equally, which affects the final prediction accuracy. In addition, convolutional networks lose key information due to downsampling operations and also suffer from the drawback of insufficient feature extraction capability. To address these issues, the two-layer attention mechanism and the Inception module are embedded in the capsule structure (mai-capsule model) for lifetime prediction. The first layer of the channel attention mechanism (CAM) evaluates the influence of various sensor information on the forecast; the second layer adds a time-step attention (TSAM) mechanism to the LSTM network to weigh the contribution of different moments of the engine's whole life cycle to the prediction, while weakening the influence of environmental noise on the prediction. The Inception module is introduced to perform multi-scale feature extraction on the weighted data to capture the degradation information to the maximum extent. Lastly, we are inspired to employ the capsule network to capture important position information of high and low-dimensional features, given its capacity to facilitate a more effective rendition of the overall features of the time-series data. The efficacy of the suggested model is assessed against other approaches and verified using the publicly accessible C-MPASS dataset. The end findings demonstrate the excellent prediction precision of the suggested approach.

GAIR-U-Net: 3D guided attention inception residual u-net for brain tumor segmentation using multimodal MRI images

Deep learning technologies have led to substantial breakthroughs in the field of biomedical image analysis. Accurate brain tumor segmentation is an essential aspect of treatment planning. Radiologists agree that manual segmentation is a difficult and time-consuming task that frequently causes delays in the diagnosing process. While U-Net-based methods have been widely used for brain tumor segmentation, many challenges persist, particularly when dealing with tumors of varying sizes, locations, and shapes. Additionally, segmenting tumor regions with structures requires a comprehensive model, which can increase computational complexity and potentially cause gradient vanishing issues. This study presents a novel method called 3D Guided Attention-based deep Inception Residual U-Net (GAIR-U-Net) to address these challenges. This model combines attention mechanisms, an inception module, and residual blocks with dilated convolution to enhance feature representation and spatial context understanding. The backbone of the model is the U-Net model, which leverages the power of inception and residual connections to capture intricate patterns and hierarchical features while expanding the model’s width in three-dimensional space without significantly increasing computational complexity. The attention mechanisms play a role in focusing on important regions and areas while downgrading irrelevant details. The dilated convolutions in the network help in learning both local and global information, improving accuracy and adaptability in segmenting tumors. All the experiments in this study were carried out on multimodal MRI scans that include (T1-weighted, T1-ce, T2-weighted, and FLAIR sequences) from the BraTS 2020 dataset. The presented model is trained and tested on the same dataset, which exhibited promising performance compared to previous methods. On the BraTS 2020 validation dataset, the proposed model obtained a dice score of 0.8796, 0.8634, and 0.8441 for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. These results demonstrate the model’s efficacy in precisely segmenting brain tumors across various modalities. Comparative analyses underscore the model’s versatility in handling tumor shape variations, size, and location, making it a promising solution for clinical applications.

Computationally efficient handwritten Telugu text recognition

<p>Optical character recognition (OCR) for regional languages is difficult due to their complex orthographic structure, lack of dataset resources, a greater number of characters and similarity in structure between characters. Telugu is popular language in states of Andhra and Telangana. Telugu exhibits distinct separation between characters within a word, making a character-level dataset sufficient. With a smaller dataset, we can effectively recognize more words. However, challenges arise during the training of compound characters, which are combinations of vowels and consonants. These are considered as two or more characters based on associated vattus and dheerghams with the base character. To address this challenge, each compound character is encoded into a numerical value and used as input during training, with subsequent retrieval during recognition. The segmentation issue arises from overlapping characters caused by varying handwritten styles. For handling segmentation issues at the character level arising from handwritten styles, we have proposed an algorithm based on the language's features. To enhance word-level accuracy a dictionary-based model was devised. A neural network utilizing the inception module is employed for feature extraction at various scales, achieving word-level accuracy rates of 78% with fewer trainable parameters.</p>

Segment anything with inception module for automated segmentation of endometrium in ultrasound images.

Accurate segmentation of the endometrium in ultrasound images is essential for gynecological diagnostics and treatment planning. Manual segmentation methods are time-consuming and subjective, prompting the exploration of automated solutions. We introduce "segment anything with inception module" (SAIM), a specialized adaptation of the segment anything model, tailored specifically for the segmentation of endometrium structures in ultrasound images. SAIM incorporates enhancements to the image encoder structure and integrates point prompts to guide the segmentation process. We utilized ultrasound images from patients undergoing hysteroscopic surgery in the gynecological department to train and evaluate the model. Our study demonstrates SAIM's superior segmentation performance through quantitative and qualitative evaluations, surpassing existing automated methods. SAIM achieves a dice similarity coefficient of 76.31% and an intersection over union score of 63.71%, outperforming traditional task-specific deep learning models and other SAM-based foundation models. The proposed SAIM achieves high segmentation accuracy, providing high diagnostic precision and efficiency. Furthermore, it is potentially an efficient tool for junior medical professionals in education and diagnosis.

Ring artifacts suppression for X-ray CT images by fusion of dual-domain images based on improved UNet

The ring artifact is a typical artifact in X-ray Computed Tomography (CT) images, the existence of which can reduce the image's quality, change the structure and details and affects the interpretation of image information. This paper conducts research from two aspects: tomographic images and sinograms, and designs a dual-domain artifact suppression network. By introducing the Inception module and the residual module, the network's ability to extract features is improved, and the gradient diffusion is suppressed. By customizing the loss function, the network's recognition of ring artifacts is improved and the network's convergence is accelerated. Finally, the outputs of the dual-domain are fused based on the Nonsubsampled Contourlet Transform (NSCT) fusion theory to improve the quality of the image. In addition, three experiments of CT scanning are designed based on microfocus cone-beam CT system to verify the effectiveness. The model is trained and tested using real datasets. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are employed as evaluation indicators of image quality. Finally, the proposed method is compared with typical algorithms for artifact suppression. The experimental results show that the method effectively protects the structure information in CT images while suppressing ring artifacts to the greatest extent. The PSNR, SSIM, and MSE of the processed images can respectively reach 36.167 dB, 0.9847 and 6.87 × e-4.

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

Due to the projected increase in food production by 70% in 2050, crops should be additionally protected from diseases and pests to ensure a sufficient food supply. Transfer deep learning approaches provide a more efficient solution than traditional methods, which are labor-intensive and struggle to effectively monitor large areas, leading to delayed disease detection. This study proposed a versatile module based on the Inception module, Mish activation function, and Batch normalization (IncMB) as a part of deep neural networks. A convolutional neural network (CNN) with transfer learning was used as the base for evaluated approaches for tomato disease detection: (1) CNNs, (2) CNNs with a support vector machine (SVM), and (3) CNNs with the proposed IncMB module. In the experiment, the public dataset PlantVillage was used, containing images of six different tomato leaf diseases. The best results were achieved by the pre-trained InceptionV3 network, which contains an IncMB module with an accuracy of 97.78%. In three out of four cases, the highest accuracy was achieved by networks containing the proposed IncMB module in comparison to evaluated CNNs. The proposed IncMB module represented an improvement in the early detection of plant diseases, providing a basis for timely leaf disease detection.

Pre-training CNN model Several Methods of Comparative Analysis

In recent years, the rapid evolution of neural networks, exemplified by models such as VGG and GoogLeNet, has reshaped the landscape of artificial intelligence. This comparative exploration, focusing on transfer learning and attention mechanisms, not only reveals subtle performance distinctions but also provides crucial insights for refining these models in practical applications. This paper delves into the comparative analysis of two prominent neural network models, VGG and GoogLeNet, within the context of image classification. VGG, known for its deep structure, and GoogLeNet, incorporating innovative inception modules, are evaluated through experiments involving flower image datasets. The study includes transfer learning and explores the integration of attention mechanisms, specifically CBAM attention, into both models. Results indicate GoogLeNet's superior performance in terms of parameter efficiency, convergence speed, and overall accuracy. Furthermore, the addition of attention mechanisms enhances classification accuracy for both models. The paper concludes with insights into potential areas for further research, emphasizing optimal attention mechanism placement and a comparison with traditional methods in future studies.

Spatio‐temporal attention modules in orientation‐magnitude‐response guided multi‐stream CNNs for human action recognition

AbstractThis paper introduces a new descriptor called orientation‐magnitude response maps as a single 2D image to effectively explore motion patterns. Moreover, boosted multi‐stream CNN‐based model with various attention modules is designed for human action recognition. The model incorporates a convolutional self‐attention autoencoder to represent compressed and high‐level motion features. Sequential convolutional self‐attention modules are used to exploit the implicit relationships within motion patterns. Furthermore, 2D discrete wavelet transform is employed to decompose RGB frames into discriminative coefficients, providing supplementary spatial information related to the actors actions. A spatial attention block, implemented through the weighted inception module in a CNN‐based structure, is designed to weigh the multi‐scale neighbours of various image patches. Moreover, local and global body pose features are combined by extracting informative joints based on geometry features and joint trajectories in 3D space. To provide the importance of specific channels in pose descriptors, a multi‐scale channel attention module is proposed. For each data modality, a boosted CNN‐based model is designed, and the action predictions from different streams are seamlessly integrated. The effectiveness of the proposed model is evaluated across multiple datasets, including HMDB51, UTD‐MHAD, and MSR‐daily activity, showcasing its potential in the field of action recognition.

Imaging in double-casing wells with convolutional neural network based on inception module

The evaluation of well integrity in double-casing wells is critical for ensuring well stability, preventing oil and gas leaks, avoiding pollution, and ensuring safety throughout well development and production. However, the current predominant method of assessing cementing quality primarily focuses on single-casing wells, with limited work conducted on double-casing wells. This study introduces a novel approach for evaluating the cementing quality using the Inception module of convolutional neural networks. First, the finite-difference method is employed to generate borehole sonic data corresponding to a variety of model configurations, which are used to train a neural network that learns spatial features from the borehole sonic data to reconstruct the slowness model. By adjusting the network architecture and parameters, it is discovered that a neural network with two blocks and 4096 nodes in the fully connected layer demonstrated the best imaging results and exhibited strong anti-noise capabilities. The proposed method is validated using practical wellbore size models, demonstrating excellent results and offering a more effective means of evaluating wellbore integrity in double-casing wells. In addition, dipole acoustic logging data are used to conduct slowness model imaging of the compressional (P-) wave and shear (S-) wave in double-casing wells to verify the feasibility of cementing quality evaluation. The developed method contributes to more accurate evaluations of wellbore integrity for the oil and gas industry, leading to improved safety and environmental outcomes.

3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches

A significant proportion of recent mineral exploration has increasingly focused on the targeting of deep-seated orebodies. Mineral prospectivity modeling is one of the more important approaches that facilitates exploration targeting and the mitigation of risks associated with mineral exploration, particularly under cover. Recent advances in 3D mineral prospectivity modeling enable the effective extraction of predictive information from three-dimensional geological models, enabling more accurate exploration targeting of deep-seated orebodies. These advancements have synergized with deep learning approaches to improve the efficiency of mineral exploration based on nonlinear and multi-layer sensing attributes, effectively enabling the identification and extraction of key relationships between 3D predictive maps and mineralization. The main deep learning method used for 3D mineral prospectivity modeling is convolutional neural network (CNN) modeling. However, this research typically does not consider the multiscale features of geological structures, meaning further improvements can be made to this modeling approach. This paper introduces a multi-scale 3D convolutional neural network model (3D CNN) incorporating a spatial attention mechanism and an Inception module (MSAM-CNN) for 3D mineral prospectivity modeling. By integrating Inception modules and spatial attention mechanisms, the network's capability to identify multi-scale geological features and delineate key predictive areas is significantly enhanced compared to typical CNN approaches. This new approach provides further improvement in the accuracy and generalization capability of 3D mineral prospectivity modeling. To evaluate the effectiveness of this model, we undertook 3D mineral prospectivity modeling within the area of the Baixiangshan iron deposit, in the Ningwu Basin of the Middle-Lower Yangtze River Metallogenic Belt, China. The results show that the multi-scale 3D convolutional neural network model is remarkably robust and has good generalization capabilities. The approach can also can effectively delineate targets within the deep and peripheral areas of the deposit, providing targets for future exploration. The addition, performance indicators, ROC curve, and Capture-Efficiency curve generated during this modeling consistently demonstrate that the MSAM-CNN model outperforms Inception-enhanced CNN (M-CNN), CNN, Random Forest (RF), and Support Vector Machine (SVM) models. All of this indicates that MSAM-CNN approaches can effectively extract 3D spatial features within 3D predictive maps during 3D mineral prospectivity modeling better than other approaches that are commonly used, indicating that thius approach represents a promising tool for the accurate and precise identification of targets during future exploration for deep-seated mineralization.

IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)

This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accuracy across various SNR(Signal-to-Noise Ratio) environments. Experimental results demonstrate IncepSeqNet’s outperformance of existing models, particularly at low SNR levels. Furthermore, we have confirmed its applicability in practical 5G systems by using real-world signal data.

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

Inception‐YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules

AbstractThe demand for less complex and more accurate architectures has always been a priority since the broad usage of computer vision in everyday life, like auto‐drive cars, portable applications, augmented reality systems, medical image analysis etc. There are a lot of methods that have been developed to improve the accuracy and complexity of object detection, like the generations of R‐CNNs and YOLOs. However, these methods are not the most efficient architectures, and there is always room to improve. In this study, the 5th version of YOLO is employed and the improved architecture, Inception‐YOLO, is presented. The model significantly outperforms the SOTA YOLO family. Specifically, the improvements can be summarised as follows: impressive improvement of floating point operations (FLOPs) and number of parameters, as well as improvement in accuracy compared to the models with fewer FLOPs. All our presented approaches, like the optimized inception module, proposed structures for CSP and SPPF, and the improved loss function used in this research, work together to incrementally improve detection results, accuracy, demanded memory, and FLOPs simultaneously. For a glimpse of performance, the Inception‐YOLO‐S model hits 38.7% AP with 5.9M parameters and 11.5 BFLOPs and outperforms YOLOv5‐S with 37.4% AP, 7.2M parameters, and 16.5 BFLOPs.

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors

Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract important feature information and adjust the weight parameters; then the feature information is input to the Improved Bi-directional Gate Recurrent Unit (IBiGRU) to extract its timing features further; finally, the fault identification is achieved by the SoftMax function. The primary hyperparameters in the model are optimized by the Weighted Mean Of Vectors Algorithm (INFO). The experimental results show that the method is effective in fault diagnosis of asynchronous motors, with an accuracy rate close to 100%, and can still maintain a high accuracy rate under the condition of low noise ratio, with good robustness and generalization ability.

Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images

Multi-class segmentation of unlabelled living cells in time-lapse light microscopy images is challenging due to the temporal behaviour and changes in cell life cycles and the complexity of these images. The deep-learning-based methods achieved promising outcomes and remarkable success in single- and multi-class medical and microscopy image segmentation. The main objective of this study is to develop a hybrid deep-learning-based categorical segmentation and classification method for living HeLa cells in reflected light microscopy images. A symmetric simple U-Net and three asymmetric hybrid convolution neural networks—VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net—were proposed and mutually compared to find the most suitable architecture for multi-class segmentation of our datasets. The inception module in the Inception-U-Net contained kernels with different sizes within the same layer to extract all feature descriptors. The series of residual blocks with the skip connections in each ResNet34-U-Net’s level alleviated the gradient vanishing problem and improved the generalisation ability. The m-IoU scores of multi-class segmentation for our datasets reached 0.7062, 0.7178, 0.7907, and 0.8067 for the simple U-Net, VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net, respectively. For each class and the mean value across all classes, the most accurate multi-class semantic segmentation was achieved using the ResNet34-U-Net architecture (evaluated as the m-IoU and Dice metrics).

Classification and identification of agricultural products based on improved MobileNetV2.

With the advancement of technology, the demand for increased production efficiency has gradually risen, leading to the emergence of new trends in agricultural automation and intelligence. Precision classification models play a crucial role in helping farmers accurately identify, classify, and process various agricultural products, thereby enhancing production efficiency and maximizing the economic value of agricultural products. The current MobileNetV2 network model is capable of performing the aforementioned tasks. However, it tends to exhibit recognition biases when identifying different subcategories within agricultural product varieties. To address this challenge, this paper introduces an improved MobileNetV2 convolutional neural network model. Firstly, inspired by the Inception module in GoogLeNet, we combine the improved Inception module with the original residual module, innovatively proposing a new Res-Inception module. Additionally, to further enhance the model's accuracy in detection tasks, we introduce an efficient multi-scale cross-space learning module (EMA) and embed it into the backbone structure of the network. Experimental results on the Fruit-360 dataset demonstrate that the improved MobileNetV2 outperforms the original MobileNetV2 in agricultural product classification tasks, with an accuracy increase of 1.86%.

Intelligent fault diagnosis of double-aligned domain adaptation based on multi-structure fusion and multi-attention mechanism

Currently, most fault diagnosis methods based on domain adaptation (DA) reduce the distribution discrepancy between the source and target domains by aligning their high-dimensional features, but do not consider the impact of the source domain decision boundary on the distribution of the target domain, which leads to slow convergence and poor generalization. Aiming at the above-mentioned issues, this paper proposes a double-aligned DA deep transfer model (MSADA) based on multi-structure fusion and multi-attention mechanism. Firstly, a multi-structure fusion network with Inception module is constructed to increase the information capacity in the extracted features and enhance the representation of deep features. Then, the multi-attention mechanism is embedded into different convolutional channels separately to learn globally and locally important information and reduce the influence of the source domain decision boundary on the target domain distribution. Finally, the multi-kernel maximum mean discrepancy and KL divergence are combined as a new double-aligned distribution discrepancy metric to align the samples and probability distributions of the source domain and the target domain, respectively. At the same time, a dynamic adaptive factor is designed to adjust the contribution of the two types of distributions, thus effectively improving the training efficiency and the robustness of the model. Through the validation analysis of two rolling bearing dataset cases, the proposed MSADA has better cross-domain diagnostic performance than other DA methods.