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global attention module and cascade fusion network for steel surface defect detection

PATTERN RECOGNITION 2025年 158卷

作者： Liu, Guanghu Chu, Maoxiang Gong, Rongfen Zheng, Zehao Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

Steel surface defect detection plays a pivotal role in contemporary society, ensuring quality and safety in construction and manufacturing, reducing production costs, improving efficiency, and driving technological innovation. However, this task encounters challenges, including addressing unstructured features, multi-scale issues, and a scarcity of available data. To overcome these challenges, this paper proposes a global attention module and cascade fusion network for steel surface defect detection, called GC-Net. In this network, the global attention module is proposed to enhance the capability of the model to handle unstructured defects. Subsequently, a cascade fusion network is designed for multi-scale feature fusion, thereby improving detection accuracy for defects of varying scales. Following this, soft non-maximum suppression is applied in the post- processing stage to eliminate redundant detection boxes, further enhancing the detection performance of the network. Finally, a series of data augmentation techniques, including oversampling and small object augmentation, are employed in the experimental sessions to mitigate the issue of data scarcity. The experimental results on two datasets for steel surface defect detection demonstrate that the proposed method outperforms state-of-the-art methods in terms of mAP50 metric (NEU-DET: 0.771, GC10-DET: 0.635).

关键词： Surface defect detection Deep learning Object detection global attention module Cascade fusion network

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Scene Chinese Recognition with Local and global attention

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PATTERN RECOGNITION 2025年 158卷

作者： Chen, Zhao Yi, Yaohua Gan, Chaohua Tang, Ziwei Kong, Dezhu Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan Hubei Peoples R China Zhuhai Pantum Elect Co Ltd Zhuhai Peoples R China

Recently, scene Chinese recognition has attracted increasing attention. While mainstream scene text recognition methods exhibit outstanding performance in English recognition, they are considerably limited in Chinese recognition, due to inter-class similarity, intra-class variability, and complex combination of components in scene Chinese text. In this paper, we design Adaptive Position Encoding(APE) to enhance the model's ability to perceive spatial information. Based on APE, we have innovatively designed Local attention module (LAM) and global attention module (GAM). Specifically, LAM captures local features to identify common characteristics among characters of the same category, addressing the issue of intra-class variability. Meanwhile, LAM captures global features to identify the subordination relationships of Chinese character components. By integrating LAM and GAM, combining both local and global features, it is possible to find differences in the details among features that are fundamentally similar, thus solving the problem of inter-class similarity. Further, we contrive the transformer encoder-decoder structure to identify the vast variety of Chinese characters. Based on the Local/global attention module and transformer encoder-decoder framework, we devise the novel sequence- to-sequence Local and global attention Network(LGANet), where both the backbone and the encoder/decoder are composed of attention mechanisms. Subsequent experiments on the Chinese scene dataset show that the recognition accuracy of our proposed LGANet is 77.3% and the normalized editing distance is 88.6%, both of which achieve the SOTA results in Fig. 1.

关键词： Scene Chinese recognition Adaptive Position Encoding Local attention module global attention module

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A spatial-temporal graph attention network for protein-ligand binding affinity prediction based on molecular geometry

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MULTIMEDIA SYSTEMS 2025年第1期31卷 1-22页

作者： Li, Gaili Yuan, Yongna Zhang, Ruisheng Lanzhou Univ Sch Informat Sci Engn Lanzhou 730000 Gansu Peoples R China

Accurately estimating the binding strength between proteins and ligands is fundamental in the field of pharmaceutical research and innovation. Previous research has largely concentrated on 1D or 2D molecular descriptors, often neglecting the pivotal 3D features of molecules that profoundly impact drug properties and target binding. This oversight has resulted in diminished predictive performance in molecule-related analyses. A comprehensive grasp of molecular properties necessitates the integration of both local and global molecular information. In this paper, we introduce a deep-learning model, termed PLGAs, which represents molecular systems as graphs based on the three-dimensional configurations of protein-ligand complexes. PLGAs consist of two components: Graph Convolution Networks (GCN) and a global attention Mechanism (GAM) network. Specifically, GCNs learn both the graph structure and node attribute information, capturing local and global information to better represent node features. GAM is then used to gather interactive edges by reducing information loss and amplifying global interactions. PLGAs were tested on the standard PDBbind refined set (v.2019) and core set (v.2016). The model demonstrated a Spearman's correlation coefficient of 0.823 on the refined set and an RMSE (Root Mean Square Error) of 1.211 kcal/mol between experimental and predicted affinities on the core set, surpassing several advanced contemporary binding affinity prediction methods. We further evaluated the efficacy of various components within our model, and the marked improvements in accuracy underscore the potential of PLGAs to significantly enhance the drug development process. Python scripts implementing various components of models are available at https://***/ligaili01/PLGAs.

关键词： 3D protein-ligand binding affinity Graph convolutions networks global attention module Binding affinity prediction

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VGGAE-Net: A Two Stream Ensemble CNN for Diabetes Retinopathy Classification 4

VGGAE-Net: A Two Stream Ensemble CNN for Diabetes Retinopath...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350349900

Diabetic retinopathy is a retinal disease that leads to vision impairment in people suffering from diabetes mellitus. Early detection of disease requires expert supervision and computer diagnosis systems to avoid vision loss. Diabetic Retinopathy grading is suffering from small lesions and data imbalance problems. In this work, we proposed a VGG based attentive ensemble Network (VGGAE-Net) for the classification of diabetes Retinopathy. We have also utilized the two attention blocks channel attention module and class attention module with extracted feature maps from the CNN. Then their prediction outputs are combined to provide the final output for the classification task. The experiment shows that the ensemble approach of two attention blocks with VGG model gives 96.66% accuracy as compared to the existing recent models. © 2024 IEEE.

关键词： class attention module diabetes retinopathy global attention module

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Ship Segmentation via Encoder-Decoder Network With global attention in High-Resolution SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Li, Jichao Gou, Shuiping Li, Ruimin Chen, Jia-Wei Sun, Xiaolong Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Shaanxi Peoples R China China Elect Technol Grp Corp 20th Res Inst Xian 710068 Shaanxi Peoples R China

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder-decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder-decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.

关键词： Marine vehicles Image segmentation Radar polarimetry Semantics Synthetic aperture radar Feature extraction Wavelet transforms Encoder-decoder structure global attention module high-resolution synthetic aperture radar (SAR) images ship segmentation

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A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks

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CONSTRUCTION AND BUILDING MATERIALS 2024年 414卷

作者： Xiong, Chenqin Zayed, Tarek Abdelkader, Eslam Mohammed Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong 999077 Peoples R China Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt

Hong Kong, among the world's most densely populated cities, has witnessed rapid growth in traffic volume, resulting in increased traffic density and vehicle loads. Regular bridge inspections are imperative to ensure human safety and safeguard property. However, conventional visual inspection methods are highly criticized for their critical limitations such as inaccuracy, subjectivity, labor-intensiveness, tediousness, and hazardousness. Cracks are regarded as the most prevalent type of defects encountered during inspection of reinforced concrete bridges. Automated detection of bridge surface cracks is a quite challenging and hectic task due to their random characteristics and usual in complex and non-uniform background textures. Presence. In light of foregoing, this paper proposes a novel computer vision model for concrete bridge crack detection in an attempt to circumvent the critical deficiencies of manual visual inspection. The developed model is envisioned on the use of you only look once version 8 (YOLOv8) architecture, which is cited as one of the most advanced convolutional neural networks structures for multi-scale object detection. Comprising three fundamental components - the backbone, neck, and head, this model introduces the concept of a decoupled head, segregating it into a detection head and a classification head. This design empowers the model with greater flexibility in handling diverse tasks. Moreover, the incorporation of the global attention module (GAM) and the wise intersection over union (IoU) loss function serves to further boost detection correctness of the developed model and amplify its generalization ability. The developed YOLOv8-GAM-Wise-IoU is compared against some of the widely acknowledged one-stage and twostage deep learning models using the evaluation metrics of precision, recall, F1-score, mean average precision (mAP) and IoU. It outperformed them accomplishing testing precision, recall, F1-score, mAP50, mAP50-95 and mAP75 of 97.4%,

关键词： Bridges Computer vision Cracks You only look once version 8 global attention module Wise intersection over union

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Topology and channel affinity reinforced global attention for person re-identification

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第9期36卷 5136-5160页

作者： Wang, Xile Gao, Chengcheng Xin, Ming Zhang, Sihan Zhang, Miaohui Henan Univ Sch Artificial Intelligence Kaifeng 475001 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng Peoples R China

Person re-identification (re-id), with the goal to recognize persons from images captured by nonoverlapping cameras, is a challenging topic in computer vision. It has been studied extensively in recent years, and the attention mechanism is widely applied in person re-id. But many works mainly focus on extracting discriminative features from local saliency regions, while ignoring some potentially global information between whole-body features and body-part features. In this study, we first proposed two effective global information for extracting discriminative features: spatial topology information (STI) and channel affinity information (CAI). On this basis, we further propose a Multi-information Fusion reinforced global attention (MIFGA) module which can effectively fuse a variety of information and utilize more comprehensive information to guide the learning of attention, so as to obtain pedestrian features that are conducive to clustering. Specifically, the proposed MIFGA module includes spatial attention (MIFGA-S) and channel attention (MIFGA-C). MIFGA-S mainly utilizes local feature semantic information and STI to guide the learning of spatial attention. Furthermore, to mine the potential topology information in original feature maps, the self-learning graph convolution network is proposed. MIFGA-C fuses channel semantic information and CAI to guide the learning of channel attention. Extensive ablation studies demonstrate that our proposed MIFGA significantly enhances the baseline model and achieves a competitive performance compared with the state-of-the-art person re-id methods on standard data sets Market-1501, DukeMTMC-reID, and CUHK03.

关键词： channel affinity global attention module person re-identification topology

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PCB Surface Defect Detection based on YOLOv8n

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2024年第12期51卷 2017-2025页

作者： You, Rui Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections and false alarms, particularly concerning micro-defects. This study proposes an enhanced PCB defect detection algorithm based on YOLOv8, which incorporates the global attention module (GAM), Partial Convolution Layer (PConv3), and a multi-head detection strategy. The GAM improves the model’s sensitivity to micro-defects by capturing and weighting global context information through spatial and channel attention mechanisms applied to the input feature map. The PConv3 optimizes feature extraction, minimizing false alarms due to information loss. The multi-head detection strategy identifies defects at varying scales, preserving detailed information and enhancing the detection of small-sized defects. Experimental results demonstrate that the improved algorithm achieves a 3.6% increase in average precision while meeting real-time detection requirements. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep Learning global attention module Multi-head Detection PCB Defect Detection PConv3 YOLOv8

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