Residual Attention Network Research Articles

An effective framework of human abnormal behaviour recognition and tracking using multiscale dilated assisted residual attention network

The prevention of certain unwanted crime events and eliminating them even before their execution can be done by automatic identification of abnormal behavior in humans. Hence automatic prediction of abnormal human behavior is a difficult task to perform. Some of the automated model has been implemented and provided the most promising results. The manual intervention is being the greatest approach in earlier time, yet it brings with numerous errors, consumes more time and more cost effective. Henceforth, the automated model is suggested for identifying the activities. As the scholar focus on machine and deep learning, this classifier may extract the hand-crafted features. But it fails to yield the appropriate solution for finding the activities. Since it belongs to the video frames, the object detection is highly Ineffective feature vector and inadequate scale measures of the learning model paves the way for performance degradation. This issue can be resolved by including an attention mechanism in the deep learning model for both monitoring and classification purposes. The recommended Human Abnormal Behavior Recognition and Tracking (HABRT) model performs the following operations, such as the collection of video, categorizing the behavior in the video as normal or abnormal, monitoring, extraction of the object, and classification of the abnormality. The input video with such frames is initially gathered from publically available databases. By using these frames, the abnormal behavior classification is done by Multiscale Dilated assisted Residual Attention Network (MD-RAN), For further enhancement, the hyper-parameters in the MD-RAN are optimally selected by novel Modified Random Parameter-based Chimp Optimization Algorithm (MRP-ChOA). Once the abnormal frames are obtained, the activity tracking is achieved by Adaptively Modified You Only Look Once (YOLO) V3 (AM-YOLO V3). This model encompasses with multiple layers, so that utilized number of layers are determined optimally using MRP-ChOA. Consequently, the objects are extracted from the abnormal frames with the help of AM-YOLO V3. Finally, the abnormalities are classified by using the same MD-RAN. At last, the performance is analyzed and validated with diverse parameters, which are then compared with other algorithms. While implementing the dataset 1, the accuracy value attains maximum in contrast with 3.14% of DTCN, 2.308% of CNN-RNN and 13.7% of ResAttenConvLSTM, correspondingly. Thus, the findings reveal that it has the potential to deliver extensive results for abnormal recognition and tracking.

Dual residual attention network for image denoising

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e. spatially variant noise) that is generated during image acquisition or transmission, which severely impedes their application in practical image denoising tasks. In this paper, we propose a novel Dual-branch Residual Attention Network (DRANet) for image denoising, which has both the merits of a wide model architecture and the attention-guided feature learning. The proposed DRANet includes two different parallel branches, which can capture complementary features to enhance the learning ability of the model. We designed a new residual attention block (RAB) and a novel hybrid dilated residual attention block (HDRAB) for the upper and lower branches, respectively. The RAB and HDRAB can capture rich local features through multiple skip connections between different convolutional layers, and the unimportant features can be dropped. Meanwhile, the long skip connections in each branch and the global feature fusion between the two parallel branches can effectively capture the global features as well. Extensive experiments demonstrate that compared with other state-of-the-art denoising methods, our DRANet can produce competitive denoising performance both on the synthetic and real-world noise removal. The code for DRANet is accessible at https://github.com/WenCongWu/DRANet.

Modified residual attention network for abnormalities segmentation and detection in WCE images

Modified residual attention network for abnormalities segmentation and detection in WCE images

Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet

Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets.

Multi-Scale Spatial–Spectral Residual Attention Network for Hyperspectral Image Classification

Hyperspectral images (HSIs) encapsulate a vast amount of information due to their expansive size and high number of channel dimensions. However, they are insufficiently utilized for ineffective feature extraction, particularly for regions with few samples and predominant edges. To fully leverage the spatial–spectral features of HSIs, a dual-branch multi-scale spatial–spectral residual attention network (MSRAN) that integrates multi-scale feature extraction with residual attention mechanisms is proposed. MSRAN independently extracts spatial and spectral features through dual branches, minimizing the interference between these features and enhancing the focus on feature extraction in different dimensions. Specifically, in the spectral feature extraction branch, diverse-scale 3D convolution kernels capture extended spectral sequence characteristics and neighborhood spectral features. The convolution fusion emphasizes the weight of the central pixel to be classified, followed by the use of spectral residual attention mechanisms to extract enhanced central-pixel spectral features. In the spatial feature extraction branch, multi-level receptive fields are utilized to extract various fine-grained spatial contours, edges, and local detailed features, which are further processed through spatial residual attention to effectively extract spatial composite features. Finally, the convolution fusion module adaptively integrates the center-enhanced spectral features with multi-level fine-grained spatial features for classification. Extensive comparative experiments and ablation studies demonstrate that MSRAN achieves highly competitive results on two classic datasets from Pavia University and Salinas as well as on a novel dataset of WHU-Hi-LongKou.

Printer source identification of quick response codes using residual attention network and smartphones

Quick response codes are widely utilized in commodity anti-counterfeiting, and their traceability via smartphones is an effective authentication method. However, quick response codes are susceptible to illegal duplication, and the images captured by smartphones are easily affected by changes in light and environmental noise, leading to unsatisfactory verification results. To address these issues, this paper introduces a novel approach by combining the squeeze-excitation attention module with bottleneck residual block. It presents a squeeze-excitation bottleneck residual network for printer source identification of quick response codes. The squeeze-excitation attention module pays more attention to the features that represent the printer attributes, reduces the interference of useless information, and has low computational consumption; while the bottleneck residual block has the advantages of few parameters, strong feature extraction capability, and good expandability. Thus, the performance can be improved effectively only with a small increasing in parameters. The experimental results verify that the proposed method achieves an accuracy of 98.77% under smartphone capture conditions, it outperforms other convolutional neural network-based methods in terms of identification accuracy. The deep learning model proposed in this paper can be generalized and applied to the printer source identification of paper content in the civil, criminal investigation and judicial fields.

Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network

The severity evaluation of Parkinson's disease (PD) is of great significance for the treatment of PD. However, existing methods either have limitations based on prior knowledge or are invasive methods. To propose a more generalized severity evaluation model, this paper proposes an explainable 3D multi-head attention residual convolution network. First, we introduce the 3D attention-based convolution layer to extract video features. Second, features will be fed into LSTM and residual backbone networks, which can be used to capture the contextual information of the video. Finally, we design a feature compression module to condense the learned contextual features. We develop some interpretable experiments to better explain this black-box model so that it can be better generalized. Experiments show that our model can achieve state-of-the-art diagnosis performance. The proposed lightweight but effective model is expected to serve as a suitable end-to-end deep learning baseline in future research on PD video-based severity evaluation and has the potential for large-scale application in PD telemedicine. The source code is available at https://github.com/JackAILab/MARNet.

DLGRAFE-Net: A double loss guided residual attention and feature enhancement network for polyp segmentation.

Colon polyps represent a common gastrointestinal form. In order to effectively treat and prevent complications arising from colon polyps, colon polypectomy has become a commonly used therapeutic approach. Accurately segmenting polyps from colonoscopy images can provide valuable information for early diagnosis and treatment. Due to challenges posed by illumination and contrast variations, noise and artifacts, as well as variations in polyp size and blurred boundaries in polyp images, the robustness of segmentation algorithms is a significant concern. To address these issues, this paper proposes a Double Loss Guided Residual Attention and Feature Enhancement Network (DLGRAFE-Net) for polyp segmentation. Firstly, a newly designed Semantic and Spatial Information Aggregation (SSIA) module is used to extract and fuse edge information from low-level feature graphs and semantic information from high-level feature graphs, generating local loss-guided training for the segmentation network. Secondly, newly designed Deep Supervision Feature Fusion (DSFF) modules are utilized to fuse local loss feature graphs with multi-level features from the encoder, addressing the negative impact of background imbalance caused by varying polyp sizes. Finally, Efficient Feature Extraction (EFE) decoding modules are used to extract spatial information at different scales, establishing longer-distance spatial channel dependencies to enhance the overall network performance. Extensive experiments conducted on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that the proposed network outperforms all mainstream networks and state-of-the-art networks, exhibiting superior performance and stronger generalization capabilities.

Image reconstruction of snapshot multispectral camera based on an attention residual network

Image reconstruction of snapshot multispectral camera based on an attention residual network

An Interpretable Siamese Attention Res‐CNN for Fingerprint Spoofing Detection

In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res‐CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res‐CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross‐material experiments and cross‐sensor experiments. Additionally, we leverage Gradient‐weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation.

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Multiscale Spectral–Spatial Attention Residual Fusion Network for Multisource Remote Sensing Data Classification

Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Underwater image enhancement based on multiscale residual attention networks

Underwater image enhancement based on multiscale residual attention networks

A Deep Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification

A Deep Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification

A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity Mapping

A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity Mapping

Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

A Two-Branch Multiscale Residual Attention Network for Single Image Super-Resolution in Remote Sensing Imagery

A Two-Branch Multiscale Residual Attention Network for Single Image Super-Resolution in Remote Sensing Imagery

A Novel Attention Residual Network Expression Recognition Method

A Novel Attention Residual Network Expression Recognition Method

GRAPE LEAF DISEASE RECOGNITION BASED ON A MULTISCALE MIXED ATTENTION RESIDUAL NETWORK

A multiscale efficient channel attention spatial-residual network (MECAS-ResNet) is proposed in this paper. On the basis of ResNet50, the training speed and model volume are reduced by group convolution. Through multiscale convolution, the spatial attention and effective channel attention are fused into the model to improve the network's attention toward various regions of lesions. A Wasserstein generative adversarial network is used for data enrichment, and the resulting images are diverse and variable. The enriched dataset is fed into the MECAS-ResNet model for identification and comparison to other models. After the improvement, the optimal recognition accuracy reached 95.31%, which is 10.02% higher than that on the original dataset; the model size is only 30.88 MB; and the average F1-score reached 95.30%. The improved model has higher recognition performance for similar diseases in different degrees, which is better than other models. The data generated by the experiment can be used for grape disease recognition, which greatly reduces the cost of data collection and provides technical support for grape disease recognition and precise drug treatment.