Channel Attention Module Research Articles

CATNet: Cascaded attention transformer network for marine species image classification

Complex physicochemical environmental effects result in the underwater species images’ highly intricate and diverse backgrounds, which poses significant challenges for identifying marine species. Deep learning techniques are used extensively for image classification thanks to their exceptional feature extraction capabilities. To address the above issues, we present a cascaded attention transformer network for marine species image classification named CATNet. More specifically, we first utilize the efficient channel attention module with depthwise convolution, which can efficiently extract the essential features of the image and reduce the computational parameters of the traditional attention module. Subsequently, we design a stacked transformer module to focus on the different features among a large amount of feature information, thereby reducing redundancy and improving?the generalization performance of CATNet. To comprehensively exploit the features extracted from different modules, we integrate the efficient channel attention and stacked transformer modules to construct a feature-cascaded module. Meanwhile, we construct a large-scale Marine Species Image Dataset (MSID) including 8605 underwater images with different species. Concretely, it consists of 712 coral reef images, 1270 fish images, 1082 people images, 2414 sea urchin images, 1219 squid images, 837 sea cucumber images, and 1071 turtle images. Comprehensive experimentation on our built dataset underscores the proposed CATNet’s superiority over the existing state-of-the-art methods in marine species image classification. The data is available. https://www.researchgate.net/publication/381961074_2024MSID.

Building a Speech Dataset and Recognition Model for the Minority Tu Language

Speech recognition technology has many applications in our daily life. However, for many low-resource languages without written forms, acquiring sufficient training data remains a significant challenge for building accurate ASR models. The Tu language, spoken by an ethnic minority group in Qinghai Province in China, is one such example. Due to the lack of written records and the great diversity in regional pronunciations, there has been little previous research on Tu-language speech recognition. This work seeks to address this research gap by creating the first speech dataset for the Tu language spoken in Huzhu County, Qinghai. We first formulated the relevant pronunciation rules for the Tu language based on linguistic analysis. Then, we constructed a new speech corpus, named HZ-TuDs, through targeted data collection and annotation. Based on the HZ-TuDs dataset, we designed several baseline sequence-to-sequence deep neural models for end-to-end Tu-language speech recognition. Additionally, we proposed a novel SA-conformer model, which combines convolutional and channel attention modules to better extract speech features. Experiments showed that our proposed SA-conformer model can significantly reduce the character error rate from 23% to 12%, effectively improving the accuracy of Tu language recognition compared to previous approaches. This demonstrates the effectiveness of our dataset construction and model design efforts in advancing speech recognition technology for this low-resource minority language.

LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.

Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objective guidance efficiency. But endometrial cytopathology images often come from different medical centers with different staining styles. It decreases the generalization ability of deep learning models in cytopathology images analysis, leading to poor performance. This study presents a robust automated screening framework for endometrial cancer that can be applied to cytopathology images with different staining styles, and provide an objective diagnostic reference for cytopathologists, thus contributing to clinical treatment. We collected and built the XJTU-EC dataset, the first cytopathology dataset that includes segmentation and classification labels. And we propose an efficient two-stage framework for adapting different staining style images, and screening endometrial cancer at the cellular level. Specifically, in the first stage, a novel CM-UNet is utilized to segment cell clumps, with a channel attention (CA) module and a multi-level semantic supervision (MSS) module. It can ignore staining variance and focus on extracting semantic information for segmentation. In the second stage, we propose a robust and effective classification algorithm based on contrastive learning, ECRNet. By momentum-based updating and adding labeled memory banks, it can reduce most of the false negative results. On the XJTU-EC dataset, CM-UNet achieves an excellent segmentation performance, and ECRNet obtains an accuracy of 98.50%, a precision of 99.32% and a sensitivity of 97.67% on the test set, which outperforms other competitive classical models. Our method robustly predicts endometrial cancer on cytopathologic images with different staining styles, which will further advance research in endometrial cancer screening and provide early diagnosis for patients. The code will be available on GitHub.

A Lightweight CNN Based on Axial Depthwise Convolution and Hybrid Attention for Remote Sensing Image Dehazing

Hazy weather reduces contrast, narrows the dynamic range, and blurs the details of the remote sensing image. Additionally, color fidelity deteriorates, causing color shifts and image distortion, thereby impairing the utility of remote sensing data. In this paper, we propose a lightweight remote sensing-image-dehazing network, named LRSDN. The network comprises two tailored, lightweight modules arranged in cascade. The first module, the axial depthwise convolution and residual learning block (ADRB), is for feature extraction, efficiently expanding the convolutional receptive field with little computational overhead. The second is a feature-calibration module based on the hybrid attention block (HAB), which integrates a simplified, yet effective channel attention module and a pixel attention module embedded with an observational prior. This joint attention mechanism effectively enhances the representation of haze features. Furthermore, we introduce a novel method for remote sensing hazy image synthesis using Perlin noise, facilitating the creation of a large-scale, fine-grained remote sensing haze image dataset (RSHD). Finally, we conduct both quantitative and qualitative comparison experiments on multiple publicly available datasets. The results demonstrate that the LRSDN algorithm achieves superior dehazing performance with fewer than 0.1M parameters. We also validate the positive effects of the LRSDN in road extraction and land cover classification applications.

A Symmetric Efficient Spatial and Channel Attention (ESCA) Module Based on Convolutional Neural Networks

A Symmetric Efficient Spatial and Channel Attention (ESCA) Module Based on Convolutional Neural Networks

A new strategy for weed detection in maize fields

Timely determination of weed distributions in fields is crucial for the precise spraying of herbicides. This facilitates weed control while saving costs and protecting the environment. Existing weed detection strategies often rely on the utilization of numerous weed samples to train detection models directly, which presents challenges in situations involving limited weed samples. To address this issue, a novel weed detection strategy was proposed in this study to identify weeds accurately in fields with varying coverage levels. For this purpose, red–green–blue (RGB) images of maize fields with different weed coverage levels were captured via a vertical take-off and landing fixed-wing unmanned aerial vehicle (UAV). The UAV images were first mosaicked, and a new weed detection strategy was developed and assessed. In this process, the MeanShift segmentation method, coupled with the local variance (LV) segmentation evaluation function and the Otsu automatic classification method, was initially employed to extract vegetation areas. The you-only-look-once (YOLO) v5n model was subsequently improved and used to detect maize plants. Finally, weed mapping was achieved by removing the identified maize plants from the vegetation through overlay analysis. The evaluation of the proposed method via an external dataset yielded favorable weed detection results, with an R2 value of 0.96 and a root mean square error (RMSE) value of 3.08 % under the different weed coverage levels. Specifically, in addition to adjusting the activation function and the nonmaximum suppression method, the impacts of integrating various attention modules at different positions on the performance of the YOLO v5n model for maize plant detection were analyzed. Improving the YOLO v5n model by incorporating the efficient channel attention (ECA) module into the backbone of the original model and utilizing the Hardswish activation function is recommended. Overall, this study offers support for precise weed control.

ICS-ResNet: A Lightweight Network for Maize Leaf Disease Classification

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation.

Custom Anchorless Object Detection Model for 3D Synthetic Traffic Sign Board Dataset with Depth Estimation and Text Character Extraction

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions.

Feature boosting with efficient attention for scene parsing

The complexity of scene parsing grows with the number of object and scene classes, which is higher in unrestricted open scenes. The biggest challenge is to model the spatial relation between scene elements while succeeding in identifying objects at smaller scales. This paper presents a novel feature-boosting network that gathers spatial context from multiple levels of feature extraction and computes the attention weights for each level of representation to generate the final class labels. A novel ‘channel attention module’ is designed to compute the attention weights, ensuring that features from the relevant extraction stages are boosted while the others are attenuated. The model also learns spatial context information at low resolution to preserve the abstract spatial relationships among scene elements and reduce computational cost. Spatial attention is subsequently concatenated into a final feature set before applying feature boosting. Low-resolution spatial attention features are trained using an auxiliary task that help to learn a coarse global scene structure. The proposed model outperforms all state-of-the-art models on both the ADE20K and the Cityscapes datasets.

MonuNet: a high performance deep learning network for Kolkata heritage image classification

Kolkata, renowned as the City of Joy, boasts a rich tapestry of cultural heritage spanning centuries. Despite the significance of its architectural marvels, accessing comprehensive visual documentation of Kolkata's heritage sites remains a challenge. In online searches, limited imagery often fails to provide a detailed understanding of these historical landmarks. To address this gap, this paper introduces MonuNet, a high-performance deep-learning network specifically designed for the classification of heritage images from Kolkata. The development of MonuNet addresses the critical need for efficient and accurate identification of Kolkata's architectural marvels, which are significant tangible cultural heritages. The dataset used to train MonuNet is organized by heritage sites, each category within the dataset represents distinct sites. It includes images from 13 prominent heritage sites in Kolkata. For each of these sites, there are 50 images, making it a structured collection where each category (heritage site) is equally represented. The proposed network utilizes a unique architecture incorporating a Dense channel attention module and a Parallel-spatial channel attention module to capture intricate architectural details and spatial relationships within the images. Experimental evaluations demonstrate the superior performance of MonuNet in classifying Kolkata heritage images with an accuracy of 89%, Precision of 87.77%, and Recall of 86.61%. The successful deployment of MonuNet holds significant implications for cultural preservation, tourism enhancement, and urban planning in Kolkata, aligning with the United Nations Sustainable Development Goals (SDGs) for sustainable city development. By providing a robust tool for the automatic identification and classification of heritage images, MonuNet promises to enrich online repositories with detailed visual documentation, thereby enhancing accessibility to Kolkata's cultural heritage for researchers, tourists, and urban planners alike.Graphical

A Novel Attention-Based Generalized Efficient Layer Aggregation Network for Landslide Detection from Satellite Data in the Higher Himalayas, Nepal

Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards.

CCBA-NMS-YD: A Vehicle Pedestrian Detection and Tracking Method Based on Improved YOLOv7 and DeepSort

In this paper, we propose a vehicle pedestrian detection and tracking method based on the improved YOLOv7 and DeepSort algorithms. We aim to improve the quality of vehicle pedestrian detection and tracking, addressing the challenges that current commercially available autonomous driving technologies face in complex and changing road traffic situations. First, the NMS (non-maximum suppression) algorithm in YOLOv7 is replaced with a modified Soft-NMS algorithm to ensure that targets can be accurately detected at high densities, and second, the CCBA (coordinate channel attention module) attention mechanism is incorporated to improve the feature extraction and perception capabilities of the network. Finally, a multi-scale feature network is introduced to extract features of small targets more accurately. Finally, the MobileNetV3 lightweight module is introduced into the feature extraction network of DeepSort, which not only reduces the number of model parameters and network complexity, but also improves the tracking performance of the target. The experimental results show that the improved YOLOv7 algorithm improves the average detection accuracy by 3.77% compared to that of the original algorithm; on the MOT20 dataset, the refined DeepSort model achieves a 1.6% increase in MOTA and a 1.9% improvement in MOTP; in addition, the model volume is one-eighth of the original algorithm. In summary, our model is able to achieve the desired real-time and accuracy, which is more suitable for autonomous driving.

Ground-truth-free Deep Learning for 3-D Seismic Denoising and Reconstruction with Channel Attention Mechanism

Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.

Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention

To overcome the challenges of feature selection in traditional machine learning and enhance the accuracy of deep learning methods for anomaly traffic detection, we propose a novel method called DCGCANet. This model integrates dilated convolution, a GRU, and a Channel Attention Network, effectively combining dilated convolutional structures with GRUs to extract both temporal and spatial features for identifying anomalous patterns in network traffic. The one-dimensional dilated convolution (DC-1D) structure is designed to expand the receptive field, allowing for comprehensive traffic feature extraction while minimizing information loss typically caused by pooling operations. The DC structure captures spatial dependencies in the data, while the GRU processes time series data to capture dynamic traffic changes. Furthermore, the channel attention (CA) module assigns importance-based weights to features in different channels, enhancing the model’s representational capacity and improving its ability to detect abnormal traffic. DCGCANet achieved an accuracy rate of 99.6% on the CIC-IDS-2017 dataset, outperforming other algorithms. Additionally, the model attained precision, recall, and F1 score rates of 99%. The generalization capability of DCGCANet was validated on a subset of CIC-IDS-2017, demonstrating superior detection performance and robust generalization potential.

Res‐MulFra: Multilevel and Multiscale Framework for Brain Tumor Segmentation

ABSTRACTIn clinical diagnosis and surgical planning, extracting brain tumors from magnetic resonance images (MRI) is very important. Nevertheless, considering the high variability and imbalance of the brain tumor datasets, the way of designing a deep neural network for accurately segmenting the brain tumor still challenges the researchers. Moreover, as the number of convolutional layers increases, the deep feature maps cannot provide fine‐grained spatial information, and this feature information is useful for segmenting brain tumors from the MRI. Aiming to solve this problem, a brain tumor segmenting method of residual multilevel and multiscale framework (Res‐MulFra) is proposed in this article. In the proposed framework, the multilevel is realized by stacking the proposed RMFM‐based segmentation network (RMFMSegNet), which is mainly used to leverage the prior knowledge to gain a better brain tumor segmentation performance. The multiscale is implemented by the proposed RMFMSegNet, which includes both the parallel multibranch structure and the serial multibranch structure, and is mainly designed for obtaining the multiscale feature information. Moreover, from various receptive fields, a residual multiscale feature fusion module (RMFM) is also proposed to effectively combine the contextual feature information. Furthermore, in order to gain a better brain tumor segmentation performance, the channel attention module is also adopted. Through assessing the devised framework on the BraTS dataset and comparing it with other advanced methods, the effectiveness of the Res‐MulFra is verified by the extensive experimental results. For the BraTS2015 testing dataset, the Dice value of the proposed method is 0.85 for the complete area, 0.72 for the core area, and 0.62 for the enhanced area.

CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.

The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation. We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions. Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations. The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Chemical composition prediction in goji (Lycium barbarum) using hyperspectral imaging and multi-task 1DCNN with attention mechanism

The bioactive components of goji berries (Lycium barbarum) are crucial determinants of their nutritional and commercial value. In this study, we combined hyperspectral imaging technology (HSI) with a one-dimensional convolutional neural network (1DCNN) to predict the content of chemical compositions in goji. To enhance the model's ability to focus on relevant information, we introduced the channel attention module (CAM), spectral attention module (SAM) and fused them together, which can focus on output features of the convolution kernels differently and adaptively emphasize more effective spectral bands, respectively. Moreover, considering the limitations of traditional single-task prediction methods and the inherent correlations among different constituents, we employed a multi-task CNN for the simultaneous prediction of various goji constituents. The results indicate that the attention-enhanced 1DCNN model outperforms both the partial least squares regression (PLSR) model and the vanilla 1DCNN. With multi-task learning, the model achieves optimal performance, achieving an average R2 of 0.9435 for the prediction of the three components. Our research develops an efficient and accurate method for predicting the constituents of goji berries, providing a valuable, convenient, and effective tool for assessing and detecting the quality of fruit products in the food industry.

HST-MRF: Heterogeneous Swin Transformer With Multi-Receptive Field for Medical Image Segmentation.

The Transformer has been successfully used in medical image segmentation due to its excellent long-range modeling capabilities. However, patch segmentation is necessary when building a Transformer class model. This process ignores the tissue structure features within patch, resulting in the loss of shallow representation information. In this study, we propose a Heterogeneous Swin Transformer with Multi-Receptive Field (HST-MRF) model that fuses patch information from different receptive fields to solve the problem of loss of feature information caused by patch segmentation. The heterogeneous Swin Transformer (HST) is the core module, which achieves the interaction of multi-receptive field patch information through heterogeneous attention and passes it to the next stage for progressive learning, thus complementing the patch structure information. We also designed a two-stage fusion module, multimodal bilinear pooling (MBP), to assist HST in further fusing multi-receptive field information and combining low-level and high-level semantic information for accurate localization of lesion regions. In addition, we developed adaptive patch embedding (APE) and soft channel attention (SCA) modules to retain more valuable information when acquiring patch embedding and filtering channel features, respectively, thereby improving model segmentation quality. We evaluated HST-MRF on multiple datasets for polyp, skin lesion and breast ultrasound segmentation tasks. Experimental results show that our proposed method outperforms state-of-the-art models and can achieve superior performance. Furthermore, we verified the effectiveness of each module and the benefits of multi-receptive field segmentation in reducing the loss of structural information through ablation experiments and qualitative analysis.

Gait recognition using deep learning with handling defective data from multiple wearable sensors

Gait recognition based on multiple wearable sensors has received widespread attention in recent years. Collecting data from various types of multimodal sensors allows for a more comprehensive capture of gait information. This effectively enhances gait recognition performance. However, the increase in the number of sensors also leads to the possibility of generating defective data during the process of data collection and processing, particularly data redundancy and data anomaly. Traditional solutions often involve complex data analysis and selection, which may result in important information being filtered out and high computational demands. To address these issues, this paper proposes a new method that combines the powerful feature extraction capabilities of convolutional neural networks. To address the issue of data redundancy, the Channel Multiscale-aware (CMSA) Module is designed. It extracts information with different receptive fields from different channels of the same feature map to directly reduce similarities in information. To address the issue of data anomaly, the Feature Combination based on Local Channel Attention (FCLCA) Module is designed to select channel segments least affected by data anomaly from the feature map. Attention mechanisms are incorporated into both the CMSA and FCLCA modules to facilitate the model in adapting and learning more crucial features. Additionally, improvements are made to their pooling structures. After numerous experiments, it was demonstrated that our model achieved superior performance in gait recognition tasks.