Channel Attention Module Research Articles

A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images

COVID-19 has affected hundreds of millions of individuals, seriously harming the global population’s health, welfare, and economy. Furthermore, health facilities are severely overburdened due to the record number of COVID-19 cases, which makes prompt and accurate diagnosis difficult. Automatically identifying infected individuals and promptly placing them under special care is a critical step in reducing the burden of such issues. Convolutional Neural Networks (CNN) and other machine learning techniques can be utilized to address this demand. Many existing Deep learning models, albeit producing the intended outcomes, were developed using millions of parameters, making them unsuitable for use on devices with constrained resources. Motivated by this fact, a novel lightweight deep learning model based on Efficient Channel Attention (ECA) module and SqueezeNet architecture, is developed in this work to identify COVID-19 patients from chest X-ray and CT images in the initial phases of the disease. After the proposed lightweight model was tested on different datasets with two, three and four classes, the results show its better performance over existing models. The outcomes shown that, in comparison to the current heavyweight models, our models reduced the cost and memory requirements for computing resources dramatically, while still achieving comparable performance. These results support the notion that proposed model can help diagnose Covid-19 in patients by being easily implemented on low-resource and low-processing devices.

Shuffled ECA-Net for stress detection from multimodal wearable sensor data

BackgroundRecently, stress has been recognized as a key factor in the emergence of individual and social issues. Numerous attempts have been made to develop sensor-augmented psychological stress detection techniques, although existing methods are often impractical or overly subjective. To overcome these limitations, we acquired a dataset utilizing both wireless wearable multimodal sensors and salivary cortisol tests for supervised learning. We also developed a novel deep neural network (DNN) model that maximizes the benefits of sensor fusion. MethodWe devised a DNN involving a shuffled efficient channel attention (ECA) module called a shuffled ECA-Net, which achieves advanced feature-level sensor fusion by considering inter-modality relationships. Through an experiment involving salivary cortisol tests on 26 participants, we acquired multiple bio-signals including electrocardiograms, respiratory waveforms, and electrogastrograms in both relaxed and stressed mental states. A training dataset was generated from the obtained data. Using the dataset, our proposed model was optimized and evaluated ten times through five-fold cross-validation, while varying a random seed. ResultsOur proposed model achieved acceptable performance in stress detection, showing 0.916 accuracy, 0.917 sensitivity, 0.916 specificity, 0.914 F1-score, and 0.964 area under the receiver operating characteristic curve (AUROC). Furthermore, we demonstrated that combining multiple bio-signals with a shuffled ECA module can more accurately detect psychological stress. ConclusionsWe believe that our proposed model, coupled with the evidence for the viability of multimodal sensor fusion and a shuffled ECA-Net, would significantly contribute to the resolution of stress-related issues.

Using convolutional neural network combined with multi-scale channel attention module to predict soil properties from visible and near-infrared spectral data

With the increasing demand for precision agriculture and sustainable land management, it is crucial to quickly and accurately predict soil properties. However, there are still challenges in predicting large-scale soil properties. This study proposes a new convolutional neural network architecture (MCA-CNN) combined with visible and near-infrared spectroscopy for predicting soil properties. The architecture introduces a multi-scale channel attention mechanism, aiming to more effectively capture complex features in the spectrum. The performance of the MCA-CNN model was tested on a large-scale dataset and compared with partial least squares (PLS), extreme gradient boosting tree (XGBoost), and conventional convolutional neural network (CNN) models. The predictive performance of different models in organic carbon (OC), nitrogen (N), pH, Clay, Salt, and Sand was analyzed. The results showed that the MCA-CNN model had the best predictive performance on all soil properties, achieving the lowest root mean square error (RMSE) and the highest coefficient of determination (R2), the R2 of OC, N, pH, Clay, Silt, and Sand are 0.95, 0.93, 0.95, 0.80, 0.58, 0.64, respectively. The RMSE of OC, N, pH, Clay, Silt, and Sand are 16.60 g kg−1, 0.96 g kg−1, 0.31, 4.89 %, 8.27 %, and 11.61 %, respectively. Further research has found that soil types (mineral soil and organic soil) affect the predictive performance of OC. It is necessary to distinguish between mineral soil and organic soil when predicting soil OC content. In addition, the effectiveness of the model transfer strategy was explored for situations with small soil sample sizes. By fine-tuning the pre-trained model, the performance of the model can be significantly improved, which provides a feasible solution for predicting soil properties in situations of data scarcity. In summary, this study not only confirms the potential application of deep learning in the field of soil science, but also provides new technological avenues for future soil management and agricultural practices.

Improved YOLOX Remote Sensing Image Object Detection Algorithm

Remote sensing image object detection is one of the core tasks of remote sensing image processing. In recent years, with the development of deep learning, great progress has been made in object detection in remote sensing. However, the problems of dense small targets, complex backgrounds and poor target positioning accuracy in remote sensing images make the detection of remote sensing targets still difficult. In order to solve these problems, this research proposes a remote sensing image object detection algorithm based on improved YOLOX-S. Firstly, the Efficient Channel Attention (ECA) module is introduced to improve the network's ability to extract features in the image and suppress useless information such as background; Secondly, the loss function is optimized to improve the regression accuracy of the target bounding box. We evaluate the effectiveness of our algorithm on the NWPU VHR-10 remote sensing image dataset, the experimental results show that the detection accuracy of the algorithm can reach 95.5%, without increasing the amount of parameters. It is significantly improved compared with that of the original YOLOX-S network, and the detection performance is much better than that of some other mainstream remote sensing image detection methods. Besides, our method also shows good generalization detection performance in experiments on aircraft images in the RSOD dataset.

Multi-Prior Driven Network for RGB-D Salient Object Detection

Most existing RGB-D salient object detection (SOD) methods rely on high-quality depth images. However, their performance is limited when processing low-quality depth maps. This paper exploits more complementary image priors to guide the model to learn on variable depth maps, and a novel multi-prior driven network called MPDNet is proposed for RGB-D SOD. MPDNet utilizes four processing pipelines to process RGB images and other priors, which include an RGB image processing pipeline, a depth map processing pipeline, a fine-grained and gradient prior processing pipeline, and an edge learning pipeline. Specifically, fine-grained and gradient priors are input to the same processing pipeline. For the depth maps, fine-grained and gradient priors, a prior channel attention module utilizes the channel attention mechanism to filter noises and highlights the salient cues. The RGB image processing pipeline uses a multi-feature progressive enhancement module to fuse and enhance features from depth maps. And a multi-feature prediction decoder decodes initial salient masks. In the edge learning pipeline, edge prior serves as an edge label and is captured by an edge capture module. Finally, the clear salient masks are obtained by fusing the salient information from the four pipelines. The experimental results on six benchmarks indicate that the proposed method outperforms thirteen state-of-the-art methods in six evaluation metrics.

Dual phase hierarchical brain tumour detection and segmentation using unet based skip guidance residual convolutional transformer

Brain tumour segmentation and classification is an important approach to understanding tumour characteristics effectively. Accurate lesion segmentation requires many image modalities with varied contrasts. As a result, manual segmentation may not be suitable for large-scale research. The research has shown interest in developing various learning algorithms to detect and segment brain tumour (BT) images accurately based on the set of experimental images. Some of the issues noticed in the existing models, such as high computational complexity, high processing time, and less accuracy, were identified. In order to overcome these existing issues, a novel Dual-phase channel attentional Residual convolutional transformer Unet model (DCARCTUnet) is introduced for segmenting brain tumours. Initially, the input images are passed into the pre-processing stage to remove noise and blur. The Efficient Net model identified the pre-processed image as either normal or tumour. The proposed segmentation model can be integrated into three stages, namely dual phase channel attention module (DCAM), residual convolutional transformer Unet model and Pyramid skip guidance module (PSM). The proposed method uses the BRATS 2020–2021 dataset to analyze the results accurately. The results showed that the proposed model has a dice coefficient value of 0.99 and a Jaccard coefficient value of 0.98. Furthermore, performance analysis is carried out to demonstrate the effectiveness of the proposed model.

Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness

The accurate segmentation of skin lesions plays an important role in the diagnosis and treatment of skin cancers. However, skin lesion areas are rich in details and local features, including the appearance, size, shape, texture, etc., which pose challenges for the accurate localization and segmentation of the target area. Unfortunately, the consecutive pooling and stride convolutional operations in existing convolutional neural network (CNN)-based solutions lead to the loss of some spatial information and thus constrain the accuracy of lesion region segmentation. In addition, using only the traditional loss function in CNN cannot ensure that the model is adequately trained. In this study, a generative adversarial network is proposed, with global and local semantic feature awareness (GLSFA-GAN) for skin lesion segmentation based on adversarial training. Specifically, in the generator, a multi-scale localized feature fusion module and an effective channel-attention module are designed to acquire the multi-scale local detailed information of the skin lesion area. In addition, a global context extraction module in the bottleneck between the encoder and decoder of the generator is used to capture more global semantic features and spatial information about the lesion. After that, we use an adversarial training strategy to make the discriminator discern the generated labels and the segmentation prediction maps, which assists the generator in yielding more accurate segmentation maps. Our proposed model was trained and validated on three public skin lesion challenge datasets involving the ISIC2017, ISIC2018, and HAM10000, and the experimental results confirm that our proposed method provides a superior segmentation performance and outperforms several comparative methods.

A deep learning approach for pose error prediction in parallel robots

Parallel robots are increasingly favored in industrial automation due to their high precision, stability, and rigidity in high-speed and heavy-load tasks. However, factors such as manufacturing tolerances, assembly deviations, loads, and environmental conditions can lead to pose inaccuracies, manifesting as both geometric and non-geometric errors. Traditional model-driven approaches effectively predict geometric errors but often neglect non-geometric factors, potentially impacting the overall accuracy. This paper presents a deep learning-based method for comprehensive pose error prediction in parallel robots, addressing both geometric and non-geometric errors. The proposed approach employs an encoder-decoder network that takes target pose inputs and outputs predicted pose errors. The encoder integrates multi-layer convolutional sparse coding (CSC) blocks and efficient channel attention (ECA) modules to extract features from the network inputs. Monte Carlo dropout is utilized to estimate prediction uncertainties, enhancing the reliability of the network predictions. Experimental validation on a Stewart platform demonstrates the high accuracy and robustness of the method under various loading conditions. Comparative analyses with several typical models affirm the superior performance of the proposed network, indicating its potential to improve the operational accuracy and reliability of parallel robots in high-precision applications.

MEDANet: More Efficient Dual Attention Network for Scene Segmentation

The dual attention module is a potent semantic segmentation technique renowned for its capabilities, yet it often faces significant computational demands and GPU memory usage. To tackle these challenges, we introduce an advanced dual perception network comprising two modules: A streamlined Multi-scale Efficient Position Attention Module (MEPAM) and an optimized Efficient Channel Attention Module (MECAM). MEPAM incorporates multi-scale global average pooling into the Position Attention Module (PAM), substantially cutting computational overhead and memory consumption without compromising performance. Meanwhile, MECAM integrates compressed convolutions into the Channel Attention Module (CAM), improving segmentation accuracy and inference speed compared to conventional methods like DANet. Our approach underwent comprehensive evaluation on a semantic segmentation benchmark dataset, showcasing superior performance. For instance, on the Cityscapes dataset, our method achieves an IoU of 82.2%. In terms of efficiency gains, MEPAM operates nearly 1.97 times faster than the standard PAM module on GPU, while requiring 7.55 times less memory with a [Formula: see text] input. Similarly, MECAM achieves approximately 2.2 times faster processing than CAM, while cutting GPU memory usage by 7.53 times. This innovative dual perception network not only enhances segmentation accuracy and speed but also addresses the computational challenges associated with traditional dual attention modules.

Fourier coded aperture transform rapid spectral imaging

The Fourier coded aperture transform hyperspectral imaging system (FCTS), which we proposed in our previous work, has excellent physical interpretability compared to other encoding methods. Due to the excellent energy concentration characteristic of Fourier encoding, it can achieve high-quality spectral imaging even under undersampling conditions. However, since the grayscale encoding method limits the system's sampling speed, we propose a binary sampling Fourier-encoded spectral imaging method. Besides, to address the significant errors of reconstructed spectral image at low sampling rates, we construct a spectral image enhancement network, which introduces spatial and channel attention modules to successively enhance the spatial and spectral information of the reconstructed spectral images. During the spectral image refinement and enhancement stage, band grouping is performed to alleviate the difficulty in feature extraction and make the training process more stable. The effectiveness of our proposed method has been validated on both public datasets and our own datasets. When the sampling times are reduced by an order of magnitude, on the three public datasets, the PSNR improved by 10.79 dB, the SSIM increased by 0.35, and the SAM decreased by 12.1° on average. On our own datasets, the PSNR improved by 12 dB, the SSIM increased by 0.24, and the SAM decreased by 6.5°.

Research on Fault Diagnosis of Rolling Bearing Based on Gramian Angular Field and Lightweight Model.

Due to the limitations of deep learning models in processing one-dimensional signal feature extraction, and high model complexity leading to low training accuracy and large consumption of computing resources, this paper innovatively proposes a rolling bearing fault diagnosis method based on Gramian Angular Field (GAF) and enhanced lightweight residual network. Firstly, the one-dimensional signal is transformed into a two-dimensional GAF image, fully preserving the signal's temporal dependency. Secondly, to address the parameter redundancy and high computational complexity of the ResNet-18 model, its residual blocks are improved. The second convolutional layer in the downsampling residual blocks is removed, traditional convolutional layers are replaced with depthwise separable convolutions, and the lightweight Efficient Channel Attention (ECA) module is embedded after each residual block. This further enhances the model's ability to capture key features while maintaining low computational cost, resulting in a lightweight model referred to as E-ResNet13. Finally, the generated GAF feature maps are fed into the E-ResNet13 model for training, and through a global average pooling layer, they are mapped to a fully connected layer for classifying the faults of rolling bearings. Verifying the superiority of the proposed GAF-E-ResNet13 model, experimental results show that the GAF image encoding method achieves higher fault recognition accuracy compared to other encoding methods. Compared with other intelligent diagnosis methods, the E-ResNet13 model demonstrates strong diagnostic performance and generalization capability under both a single condition and complex varying conditions, fully proving the innovation and practicality of this method.

Triple-Stream Commonsense Circulation Transformer Network for Image Captioning

Traditional image captioning methods only have a local perspective at the dataset level, allowing them to explore dispersed information within individual images. However, the lack of a global perspective prevents them from capturing common characteristics among similar images. To address the limitation, this paper introduces a novel Triple-stream Commonsense Circulating Transformer Network (TCCTN). It incorporates contextual stream into the encoder, combining enhanced channel stream and spatial stream for comprehensive feature learning. The proposed commonsense-aware contextual attention (CCA) module queries commonsense contextual features from the dataset, obtaining global contextual association information by projecting grid features into the contextual space. The pure semantic channel attention (PSCA) module leverages compressed spatial domain for channel pooling, focusing on attention weights of pure channel features to capture inherent semantic features. The region spatial attention (RSA) module enhances spatial concepts in semantic learning by incorporating region position information. Furthermore, leveraging the complementary differences among the three features, TCCTN introduces the mixture of experts strategy to enhance the unique discriminative ability of features and promote their integration in textual feature learning. Extensive experiments on the MS-COCO dataset demonstrate the effectiveness of contextual commonsense stream and the superior performance of TCCTN.

Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection

Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance.

Reconstruction residual network with a fused spatial-channel attention mechanism for automatically classifying diabetic foot ulcer.

Diabetic foot ulcer (DFU) is a common chronic complication of diabetes. This complication is characterized by the formation of ulcers that are difficult to heal on the skin of the foot. Ulcers can negatively affect patients' quality of life, and improperly treated lesions can result in amputation and even death. Traditionally, the severity and type of foot ulcers are determined by doctors through visual observations and on the basis of their clinical experience; however, this subjective evaluation can lead to misjudgments. In addition, quantitative methods have been developed for classifying and scoring are therefore time-consuming and labor-intensive. In this paper, we propose a reconstruction residual network with a fused spatial-channel attention mechanism (FARRNet) for automatically classifying DFUs. The use of pseudo-labeling and Data augmentation as a pre-processing technique can overcome problems caused by data imbalance and small sample size. The developed model's attention was enhanced using a spatial channel attention (SPCA) module that incorporates spatial and channel attention mechanisms. A reconstruction mechanism was incorporated into the developed residual network to improve its feature extraction ability for achieving better classification. The performance of the proposed model was compared with that of state-of-the-art models and those in the DFUC Grand Challenge. When applied to the DFUC Grand Challenge, the proposed method outperforms other state-of-the-art schemes in terms of accuracy, as evaluated using 5-fold cross-validation and the following metrics: macro-average F1-score, AUC, Recall, and Precision. FARRNet achieved the F1-score of 60.81%, AUC of 87.37%, Recall of 61.04%, and Precision of 61.56%. Therefore, the proposed model is more suitable for use in medical diagnosis environments with embedded devices and limited computing resources. The proposed model can assist patients in initial identifications of ulcer wounds, thereby helping them to obtain timely treatment.

Driver distraction and fatigue detection in images using ME‐YOLOv8 algorithm

AbstractDriving while inattentive or fatigued significantly contributes to traffic accidents and puts road users at a significantly higher risk of collision. The rise in road accidents due to driver inattention resulting from distractive objects, for example, mobile phones, drinking, or tiredness, requires intelligent traffic monitoring systems to promote road safety. However, outdated detection technologies cannot handle the poor accuracy and the lack of real‐time processing possibility especially when combined with the variations of driving environment. This paper introduces “ME‐YOLOv8” which operates driver`s distraction and fatigue through a modified version of YOLOv8, which includes modules multi‐head self‐attention (MHSA) and efficient channel attention (ECA) modules applied, where the goal of MHSA is to improve the sensitivity of global features and the ECA attentions focus on critical features. Additionally, a dataset was created containing 3660 images covering multiple distracted and drowsy driver scenarios. The results reflect the enhanced detection capabilities of ME‐YOLOv8 and demonstrate its effectiveness in real‐time scenarios. This study demonstrates a significant advancement in the application of AI to public safety and highlights the critical role that state‐of‐the‐art deep learning algorithms play in lowering the risks associated with distracted and tired driving.

A Novel Skip-Connection Strategy by Fusing Spatial and Channel Wise Features for Multi-Region Medical Image Segmentation.

Recent methods often introduce attention mechanisms into the skip connections of U-shaped networks to capture features. However, these methods usually overlook spatial information extraction in skip connections and exhibit inefficiency in capturing spatial and channel information. This issue prompts us to reevaluate the design of the skip-connection mechanism and propose a new deep-learning network called the Fusing Spatial and Channel Attention Network, abbreviated as FSCA-Net. FSCA-Net is a novel U-shaped network architecture that utilizes the Parallel Attention Transformer (PAT) to enhance the extraction of spatial and channel features in the skip-connection mechanism, further compensating for downsampling losses. We design the Cross-Attention Bridge Layer (CAB) to mitigate excessive feature and resolution loss when downsampling to the lowest level, ensuring meaningful information fusion during upsampling at the lowest level. Finally, we construct the Dual-Path Channel Attention (DPCA) module to guide channel and spatial information filtering for Transformer features, eliminating ambiguities with decoder features and better concatenating features with semantic inconsistencies between the Transformer and the U-Net decoder. FSCA-Net is designed explicitly for fine-grained segmentation tasks of multiple organs and regions. Our approach achieves over 48% reduction in FLOPs and over 32% reduction in parameters compared to the state-of-the-art method. Moreover, FSCA-Net outperforms existing segmentation methods on seven public datasets, demonstrating exceptional performance.

Object Counting via Group and Graph Attention Network.

Object counting, defined as the task of accurately predicting the number of objects in static images or videos, has recently attracted considerable interest. However, the unavoidable presence of background noise prevents counting performance from advancing further. To address this issue, we created a group and graph attention network (GGANet) for dense object counting. GGANet is an encoder-decoder architecture incorporating a group channel attention (GCA) module and a learnable graph attention (LGA) module. The GCA module groups the feature map into several subfeatures, each of which is assigned an attention factor through the identical channel attention. The LGA module views the feature map as a graph structure in which the different channels represent diverse feature vertices, and the responses between channels represent edges. The GCA and LGA modules jointly avoid the interference of irrelevant pixels and suppress the background noise. Experiments are conducted on four crowd-counting datasets, two vehicle-counting datasets, one remote-sensing counting dataset, and one few-shot object-counting dataset. Comparative results prove that the proposed GGANet achieves superior counting performance.

Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement

The SAR system possesses the ability to carry out all-day and all-weather imaging, which is highly valuable in the application of aircraft identification. However, aircraft identification from SAR images still faces great challenges due to speckle noise interference, multiscale problems, and complex background interference. To solve these problems, an efficient bidirectional path multiscale fusion and attention network (EBMA-Net) is proposed in this paper. It employs bidirectional connectivity to fuse the features of aircraft with different scales to perform the accurate detection of aircraft even when the background is highly complex. In the presented EBMA-Net, a module called efficient multiscale channel attention fusion (EMCA) and three parallel squeeze efficient channel attention (SECA) modules are proposed. In the EMCA module, the bidirectional paths are created by stacking upper and lower fusion modules, which effectively integrate shallow detailed features and deep semantic information. So, the detection performance of aircraft at different scales is improved. In the SECA module, the dependency relationship between feature channels is explicitly modeled, which can automatically learn the importance of different channels, prioritize key features, so as to improve the precision and robustness of aircraft identification. In the experiment, the public dataset of aircraft identification (i.e., SAR-AIRcraft-1.0, which is generated from the GF-3 satellite) from high-resolution SAR systems is used, and several other excellent target-detection networks are used for performance comparison, namely, YOLOv5s, YOLOv7, MGCAN, and EBPA2N. According to the results, the average aircraft detection accuracy of EBMA-Net is 91.31%, which is 4.5% higher than YOLOv7; and the false alarm rate is decreased by 5%. Its accuracy in the identification of aircraft can reach 95.6%, which is about 3.7% higher than YOLOv7. Therefore, the EBMA-Net obviously outperforms the other networks for aircraft detection and identification. The proposed EBMA-Net, which can capture the detailed information and better restrain the background interference, could also be used to perform the detection and identification of dense targets with different scales and background from SAR images.

Multihead-Res-SE Residual Network with Attention for Human Activity Recognition

Human activity recognition (HAR) typically uses wearable sensors to identify and analyze the time-series data they collect, enabling recognition of specific actions. As such, HAR is increasingly applied in human–computer interaction, healthcare, and other fields, making accurate and efficient recognition of various human activities. In recent years, deep learning methods have been extensively applied in sensor-based HAR, yielding remarkable results. However, complex HAR research, which involves specific human behaviors in varied contexts, still faces several challenges. To solve these problems, we propose a multi-head neural network based on the attention mechanism. This framework contains three convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The model uses a channel attention module (squeeze–excitation module) to enhance the representational capabilities of convolutional neural networks. We conducted experiments on two publicly available benchmark datasets, UCI-HAR and WISDM, to evaluate our model. The results were satisfactory, with overall recognition accuracies of 96.72% and 97.73% on their respective datasets. The experimental results demonstrate the effectiveness of the network structure for the HAR, which ensures a higher level of accuracy.