Convolutional Attention Module Research Articles

User emotion recognition and indoor space interaction design: a CNN model optimized by multimodal weighted networks

In interior interaction design, achieving intelligent user-interior interaction is contingent upon understanding the user’s emotional responses. Precise identification of the user’s visual emotions holds paramount importance. Current visual emotion recognition methods rely solely on singular features, predominantly facial expressions, resulting in inadequate coverage of visual characteristics and low recognition rates. This study introduces a deep learning-based multimodal weighting network model to address this challenge. The model initiates with a convolutional attention module, employing a self-attention mechanism within a convolutional neural network (CNN). As a result, the multimodal weighting network model is integrated to optimize weights during training. Finally, a weight network classifier is derived from these optimized weights to facilitate visual emotion recognition. Experimental outcomes reveal a 77.057% correctness rate and a 74.75% accuracy rate in visual emotion recognition. Comparative analysis against existing models demonstrates the superiority of the multimodal weight network model, showcasing its potential to enhance human-centric and intelligent indoor interaction design.

Music source feature extraction based on improved attention mechanism and phase feature

Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition.

Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images

Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an automatic detecting method for coronary artery vulnerable plaque based on CTA image sequence. We collected CTA image sequences from 132 patients and obtained the coronary artery tree by performing three-dimensional (3D) segmentation. The coronary artery centerline was extracted using the skeleton thinning method. To address the issue of insufficient features in a single CPR image, we constructed Curved Planar Reconstructed (CPR) images from multiple directions perpendicular to the coronary artery, following the centerline. In total, we established 8797 images in the training dataset and 1553 images in the test dataset. We proposed an attentive detection method that fused a new Cascade RCNN ResNet50-FPN neural network model with a convolutional attention module (CBAM). This method achieved automatic recognition and detection of plaques on the two-dimensional multi-angle fused CPR images. The proposed ResNet50-FPN network performed feature extraction, and CBAM was added after the first and last convolutional layers to enhance the network’s attention to plaque features. Our experimental results demonstrate that deep learning neural networks can provide a feasible approach to automatically detecting coronary artery plaques. Our method achieved a plaque detection accuracy of 94.6%, outperforming the RCNN method proposed by Zriek.

A DNN robust video watermarking method in dual-tree complex wavelet transform domain

Deep learning is increasingly being applied in the field of robust watermarking. However, the existing deep learning-based video watermarking methods only uses spatial domain information as the input and the robustness against attacks such as H.264/AVC compression is still not strong. Therefore, this paper proposes a deep learning-based robust video watermarking method in dual-tree complex wavelet transform (DT-CWT) domain. The video frames are transformed into the DT-CWT domain and the suitable high-pass subbands are selected as candidate embedding positions. Then, the 2D and 3D convolutions are combined to extract both intra-frame spatial features and inter-frame temporal features for finding the stable and imperceptible coefficients for watermark embedding in the candidate positions. The convolutional attention module (CBAM) is used to further adjust the embedding coefficients and strengths. In addition, the attack layer, where a differentiable proxy is specially designed in this paper for the simulation of non-differentiable H.264/AVC compression, is introduced to generate distorted watermarked videos for improving the robustness against different attacks. Experimental results show that our method is superior to both the existing deep learning-based methods and traditional methods in the robustness against both spatial and temporal attacks while preserving high video quality. The source code is available at https://github.com/imagecbj/A-DNN-Robust-Video-Watermarking-Method-in-DT-CWT-Domain.

Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography

BackgroundTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.MethodsThis study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.ResultsThe study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.ConclusionThe AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.Graphical

A Lightweight CER-YOLOv5s Algorithm for Detection of Construction Vehicles at Power Transmission Lines

In the context of power-line scenarios characterized by complex backgrounds and diverse scales and shapes of targets, and addressing issues such as large model parameter sizes, insufficient feature extraction, and the susceptibility to missing small targets in engineering-vehicle detection tasks, a lightweight detection algorithm termed CER-YOLOv5s is firstly proposed. The C3 module was restructured by embedding a lightweight Ghost bottleneck structure and convolutional attention module, enhancing the model’s ability to extract key features while reducing computational costs. Secondly, an E-BiFPN feature pyramid network is proposed, utilizing channel attention mechanisms to effectively suppress background noise and enhance the model’s focus on important regions. Bidirectional connections were introduced to optimize the feature fusion paths, improving the efficiency of multi-scale feature fusion. At the same time, in the feature fusion part, an ERM (enhanced receptive module) was added to expand the receptive field of shallow feature maps through multiple convolution repetitions, enhancing the global information perception capability in relation to small targets. Lastly, a Soft-DIoU-NMS suppression algorithm is proposed to improve the candidate box selection mechanism, addressing the issue of suboptimal detection of occluded targets. The experimental results indicated that compared with the baseline YOLOv5s algorithm, the improved algorithm reduced parameters and computations by 27.8% and 31.9%, respectively. The mean average precision (mAP) increased by 2.9%, reaching 98.3%. This improvement surpasses recent mainstream algorithms and suggests stronger robustness across various scenarios. The algorithm meets the lightweight requirements for embedded devices in power-line scenarios.

Gca-pvt-net: group convolutional attention and PVT dual-branch network for oracle bone drill chisel segmentation

Oracle bones (Obs) are a significant carrier of the shang dynasty civilization, primarily consisting of tortoise shells and animal bones, through the study of which we can gain a deeper understanding of the political, economic, religious, and cultural aspects of the shang dynasty. The oracle bone drill chisel (Obdc) is considered an essential non-textual material. The segmentation of Obdc assists archaeologists determine the approximate age of the Obs, which possesses considerable research value. However, the breakage of thousands of years of underground buried Obs, the blurring of the edges of the area burned by the Obdc, the different shapes, and the inconsistent number have brought challenges to the accurate segmentation of the Obdc. In this article, we propose a group convolutional attention and pvt dual-branch network (GCA-PVT-Net) for Obdc segmentation. To our knowledge, this paper is the first to research the automatic segmentation of Obdc. It is a hybrid Convolutional neural network (CNN) and Transformer framework. The work offers the following contributions: (1) The Obdc images are labeled based on the delineation criteria of different drill chisel (DC) shapes to create the Obdc dataset. (2) A convolutional attention module (CAM) is proposed as both an encoder and decoder. The feature extraction process, which effectively integrates global and local information, ensures better modeling of long-term correlations in images while preserving details. (3) A channel feature aggregation module (CFAM) is designed to enhance the effective integration of channel features, enabling feature fusion across various branches and at different levels. (4) The edge deep supervision strategy is applied to smooth the jagged edge of the predicted images at the decoder’s end. Extensive experiments on the Obdc dataset show that GCA-PVT-Net outperforms other state-of-the-art (SOTA) methods. The comparative experimental results show that the edge accuracy and segmentation accuracy of the model reach the top 1.

Surface Defect Detection for Aerospace Aluminum Profiles with Attention Mechanism and Multi-Scale Features

A YOLOv5 aluminum profile defect detection algorithm that integrates attention and multi-scale features is proposed in this paper to address the issues of the low detection accuracy, high false detection rates, and high missed detection rates that are caused by the large-scale variation of surface defects, inconspicuous small defect characteristics, and a lack of concentrated feature information in defect areas. Firstly, an improved CBAM (Channel-Wise Attention Module) convolutional attention module is employed, which effectively focuses on the feature information of defect areas in the aluminum defect dataset with only a small amount of spatial dimension. Secondly, a bidirectional weighted feature fusion network is utilized, incorporating a multi-scale feature fusion network with skip connections to aggregate various high-resolution features, thus enriching the semantic expression of features. Then, new size feature maps that have not been fused are introduced into the detection layer network to improve the detection effect of small target defects. Experimental results indicate that an average detection accuracy (mAP) of 82.6% was achieved by the improved YOLOv5 algorithm on the aluminum surface defect dataset. An improvement of 6.2% over the previous version was observed. The current defect detection requirements of aluminum profile production sites are met by this enhanced algorithm.

Denoising low SNR electromagnetic conducted emissions using an improved DnCNN based mode

Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data.

A novel recognition method for complex power quality disturbances based on Markov transition field and improved densely connected network

In order to solve the difficulty that complex power quality disturbances (PQDs) are difficult to recognize accurately and efficiently under the new power system background, this paper proposes a novel PQDs recognition method based on markov transition field (MTF) and improved densely connected network (DenseNet). Firstly, the one-dimensional PQDs signal is mapped into the two-dimensional image with clear texture features by using MTF encoding method. Then, a DenseNet-S lightweight network is designed and the convolutional attention module (CBAM) is introduced to improve its feature extraction ability, so as to enhance the performance of the network. Finally, the images are input into the improved model for training and learning, and PQDs recognition is realized through the optimal model. In order to verify the effectiveness of the proposed method, experimental tests are carried out based on IEEE 1159 standard simulation dataset and real-world field measured signals dataset, and compared with existing recognition methods. The results show that the proposed method can effectively improve the recognition accuracy and noise robustness of complex PQDs, and has more advantages in disturbances recognition efficiency. It can meet the recognition accuracy and efficiency requirements of massive and complex PQDs events in engineering applications.

Seismic vulnerability signal analysis of low tower cable-stayed bridges method based on convolutional attention network

Abstract Due to the particularity and complexity of sedimentary environments, the wave impedance differences between different reflection interfaces in underground media may vary greatly. Therefore, an encoder–decoder neural network is proposed to enhance erroneous seismic weak reflection signals. The convolutional neural network (CNN) has the problem of difficulty in parallel computing, resulting in slow network training and computational efficiency. Considering that attention has an innate global self-attention mechanism, can compensate for long-term dependency deficiencies, and has the ability to perform parallel computing, which greatly compensates for the shortcomings of CNNs and recurrent neural networks, a seismic impedance inversion method based on convolutional attention networks is proposed. To improve the ability to extract noise, residual structure and convolutional attention module (CBAM) were introduced. The residual structure utilizes residual jump to weaken network degradation and reduce the difficulty of feature mapping. The CBAM uses a mixed attention weight of channel and space, which can enhance features with high correlation and suppress features with low correlation. In the decoder, in order to improve the dimension recovery ability of feature fusion, bilinear interpolation is selected for upsampling. The application results of the model and actual data indicate that this method can effectively enhance the weak reflection signals caused by the formation itself and improve the reservoir identification ability of seismic data.

Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model

The detection of black and odorous water using remote sensing technology has become an effective method. The high-resolution remote sensing images can extract target features better than low-resolution images. However, the high-resolution images often introduce complex background details and intricate textures, which often have problems with accurate feature extraction. In this paper, based on remote sensing images acquired by the Gaofen-2 satellite, we proposed a Modified DeepLabv3+ model to detect black and odorous water. To reduce the complexity of the encoder part of the model, Modified Deeplabv3+ incorporates a lightweight MobileNetV2 network. A convolutional attention module was introduced to improve the focus on the features of black and odorous water. Then, a fuzzy block was crafted to reduce the uncertainty of the raw data. Additionally, a new loss function was formulated to solve the problem of category imbalance. A series of experiments were conducted on both remote sensing images for the black and odorous water detection (RSBD) dataset and the water pollution dataset, demonstrating that the Modified DeepLabv3+ model outperforms other commonly used semantic segmentation networks. It effectively captures detailed information and reduces image segmentation errors. In addition, in order to better identify black and odorous water and enrich the spectral information of the image, we have generated derived bands using the black and odorous water index. These derived bands were fused together with the original image to construct the RSBD-II dataset. The experimental results show that adding a black and odorous water feature index can achieve a better detection effect.

BGNet: Boundary‐guided network for polyp segmentation

AbstractColonoscopy is one of the most direct and effective methods for detecting colon polyps; they are crucial for early screening and prevention of colorectal cancer (CRC). Accurate segmentation of polyp images is significant for the clinical management and treatment of CRC. However, polyp image segmentation is a challenging task because polyps vary in size, shape, and color, and have low contrast with the surrounding tissue and mucosa. To address these challenges, we propose a novel network called BGNet for polyp segmentation. BGNet consists of three modules: a boundary feature extraction module (BFEM), a mutual optimization module (MOM), and a convolutional attention module (CBAM). The BFEM extracts boundary features and predicts boundary maps, which complement and guide region features to accurately predict polyp regions and help BGNet generate precise prediction masks. The MOM optimizes the extracted features to enhance fine‐grained feature representation. The CBAM performs attention operations on the feature map to focus on informative regions and suppress irrelevant information. We conduct quantitative and qualitative evaluations on five benchmark datasets, and the results show that BGNet outperforms other methods and has strong learning ability and generalization ability.

An effective fusion model for seizure prediction: GAMRNN.

The early prediction of epileptic seizures holds paramount significance in patient care and medical research. Extracting useful spatial-temporal features to facilitate seizure prediction represents a primary challenge in this field. This study proposes GAMRNN, a novel methodology integrating a dual-layer gated recurrent unit (GRU) model with a convolutional attention module. GAMRNN aims to capture intricate spatial-temporal characteristics by highlighting informative feature channels and spatial pattern dynamics. We employ the Lion optimization algorithm to enhance the model's generalization capability and predictive accuracy. Our evaluation of GAMRNN on the widely utilized CHB-MIT EEG dataset demonstrates its effectiveness in seizure prediction. The results include an impressive average classification accuracy of 91.73%, sensitivity of 88.09%, specificity of 92.09%, and a low false positive rate of 0.053/h. Notably, GAMRNN enables early seizure prediction with a lead time ranging from 5 to 35 min, exhibiting remarkable performance improvements compared to similar prediction models.

Automatic endoscopic navigation based on attention-based network for Nasotracheal Intubation

Nasotracheal intubation (NTI) is one of the most commonly performed procedures in anesthesia and is considered the gold standard for securing the airway of patients. Endoscope operation is critical to the success of NTI. However, this operation remains challenging as it requires the surgeon to classify anatomical landmarks and detect heading targets of the endoscope tip in a sequence of monocular images. To alleviate this problem, this study presents a learning-based navigation method that automatically classifies four different anatomical landmarks and detects the heading target of the endoscope tip from endoscopic images. First, an end-to-end multitask network is introduced that consists of a branch for anatomical landmark classification and another for heading target detection. In addition, a convolutional attention module is designed to improve network performance by combining spatial and channel attention. Second, an endoscopic dataset named intuNav is built for network training. The trained network calculates navigation information without image prior knowledge. Finally, extensive experiments on the built dataset and endoscopic videos demonstrate the high performance of our method, achieving classification (94%) and detection (79.4%) accuracies. The results also indicate that the proposed method is effective and efficient in navigation generation during NTI.

Zero-sample mural superresolution reconstruction for enhanced perceptual quality

Aiming at the problem of texture loss and poor perceptual quality in low-resolution mural images, this paper proposes a zero-sample mural superresolution reconstruction method called EPZSSR to enhance perceptual quality, and the specific model is obtained by training the image. The algorithm takes the zero-shot superresolution method as the framework, randomly cuts the original image into a 128 * 128 size, performs Gaussian blurring on the image, uses Lanczos interpolation to downsample the smooth image to reduce artifacts, and uses convolutional attention module and skip connection to optimize the network structure. SmoothL1Loss is used to enhance the robustness of the model, and the PI value is introduced as the perceptual quality evaluation index. The experimental results show that compared with other superresolution reconstruction algorithms, the peak signal-to-noise ratio of the algorithm in this paper is increased by 0.98–3.23 dB on average. The mural texture reconstruction effect is better, the PI value is reduced by 0.56 on average, the mural perception quality is better, and the running time is reduced by 89.68 s on average. It has a certain value for mural superresolution reconstruction.

A Multi-Scale Pseudo-Siamese Network with an Attention Mechanism for Classification of Hyperspectral and LiDAR Data

For the remote sensing classification task, the ability of a single data source to identify the ground objects remains limited due to the lack of feature diversity. As the typical remote sensing data sources, hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data can provide complementary spectral features and elevation information, respectively. To enhance classification ability, a multi-scale Pseudo-Siamese Network with attention mechanism (MA-PSNet) is proposed by fusing HSI and LiDAR data. In the network, two sub-branch networks are designed for extracting the features from HSI and LiDAR, respectively, and the connection is further established between these two branches. Specifically, a multi-scale feature learning module is incorporated, enabling the image features to be fully extracted at different scales. Similarly, a convolutional attention module is also embedded to highlight the saliency information of the objects, which makes the network training can be more targeted, thereby eventually improving the model performance for classification. The evaluation experiments of the proposed model are carried out on an urban dataset from Houston, USA, and a rural dataset from Trento, Italy. The overall accuracy (OA) of the model can reach 95.03% on the Houston data and 99.16% on the Trento data. The experimental results fully demonstrate that the proposed model has competitive performance compared with several state-of-the-art methods.

Crop Classification of Multitemporal PolSAR Based on 3-D Attention Module With ViT

Multi-temporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multi-temporal polarimetric SAR data classification methods, the simultaneous temporal-polarimetric-spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this paper, we propose a classification model that combines a dual-stream network as a temporal-polarimetric-spatial feature extraction module with Vision Transformer(ViT) called Temporal-Polarimetric-Spatial Transformer(TSPT) to address the above problems. Secondly, a 3 dimension(3D) convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet.

Dual-attention assisted deep reinforcement learning algorithm for energy-efficient resource allocation in Industrial Internet of Things

This paper investigates the distributed resource allocation problem for energy-efficient data forwarding in resource-constrained Industrial Internet of Things (IIoT). We formulate this problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose a novel energy-efficient resource allocation algorithm based on the dual-attention assisted deep reinforcement learning (DRL) model, namely DADR, which adopts the centralized training and distributed executed (CTDE) framework to provide the intelligent decision-making ability for resource-constrained nodes. In DADR, we design a multi-scale convolutional attention module (CAM) in actor network that can extract the local state’s feature information from different dimensions; meanwhile, we propose a novel critic network based on dual-attention module and experience reconstruction module which provides a more objective and correct state evaluation from a global perspective. Moreover, the proposed critic network can solve the partially observable and non-stationary problems in multi-agent systems and can flexibly scale without changing the model structure even in a dynamic environment. Furthermore, the cooperation between CAM and multi-head self-attention (MHSA) in DADR improves the representation learning ability of DRL and offers better optimization direction for the DRL model to maximize the energy-efficient and data transmission reliability. Simulation results demonstrate that the proposed DADR algorithm outperforms the existing resource allocation algorithms and MARL models in terms of network lifetime, transmission reliability, and network stability, respectively.

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision mAP@0.5 of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection.