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A vehicle damage classification model incorporating dual attention and weighted dynamic convolution
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Yongjie ZHAI, Zixuan WANG, Zhenqi ZHANG, Xunqi ZHOU, Qianming WANG
Journal of Graphics | 2026, 47(1) : 17 - 28
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Journal of Graphics | 2026, 47(1): 17-28
Image Processing and Computer Vision
A vehicle damage classification model incorporating dual attention and weighted dynamic convolution
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Yongjie ZHAI, Zixuan WANG, Zhenqi ZHANG, Xunqi ZHOU, Qianming WANG
Affiliations
  • Department of Automation, North China Electric Power University, Baoding Hebei 071003, China
Published: 2026-02-28 doi: 10.11996/JG.j.2095-302X.2026010017
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To address the challenges of morphological similarity and the resulting difficulty in classifying vehicle damage images uploaded by clients for auto insurance claims, a model named ResAWDNet was proposed for vehicle damage classification. Firstly, to effectively augment the model’s capacity for extracting damage features, the traditional down sampling operation was replaced with weighted dynamic convolution. This approach dynamically adjusted the weights of convolutional kernels based on the input features, thereby enhancing the model’s adaptability to features of varying scales and orientations. As a result, it enabled more precise capture of the subtle differences in vehicle damage. Secondly, to ensure that the model could concentrate on the salient discriminative regions and feature channels within the images, a dual attention mechanism was embedded after the convolutional layers of the backbone network. This mechanism concurrently learned the important weights in both spatial and channel dimensions, significantly enhancing the model’s ability to capture crucial information. Consequently, it further enhanced the decision-making accuracy of the model in the task of vehicle damage classification. Finally, experimental validation was conducted based on a dataset of vehicle damage images sourced from real accident cases. The experimental results demonstrated that the ResAWDNet model was feasible and offered significant advantages for vehicle damage classification tasks, achieving an accuracy rate of 73.79%. Compared with baseline models, ResAWDNet achieved higher accuracy in classifying multiple types of damages, robustly validating the effectiveness of the proposed model.

intelligent damage assessment  /  image classification  /  deep learning  /  attention mechanism  /  dynamic convolution
Yongjie ZHAI, Zixuan WANG, Zhenqi ZHANG, Xunqi ZHOU, Qianming WANG. A vehicle damage classification model incorporating dual attention and weighted dynamic convolution[J]. Journal of Graphics, 2026 , 47 (1) : 17 -28 . DOI: 10.11996/JG.j.2095-302X.2026010017
  • National Natural Science Foundation of China(62373151)
  • Natural Science Foundation of Hebei Province(F2023502010)
  • Joint Funds of the National Natural Science Foundation of China(U21A20486)
  • Project for Cultivating the Innovative Ability of Full-time Postgraduate Students Studying in Hebei Province(CXZZSS2025152)
Year 2026 volume 47 Issue 1
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Article Info
doi: 10.11996/JG.j.2095-302X.2026010017
  • Receive Date:2025-02-28
  • Online Date:2026-05-19
  • Published:2026-02-28
Article Data
Affiliations
History
  • Received:2025-02-28
  • Accepted:2025-06-23
Funding
National Natural Science Foundation of China(62373151)
Natural Science Foundation of Hebei Province(F2023502010)
Joint Funds of the National Natural Science Foundation of China(U21A20486)
Project for Cultivating the Innovative Ability of Full-time Postgraduate Students Studying in Hebei Province(CXZZSS2025152)
Affiliations
    Department of Automation, North China Electric Power University, Baoding Hebei 071003, China

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WANG Qianming,E-mail:
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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