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<p>Automobile seats not only fulfill the traditional functions of comfort and safety but also increasingly incorporate various intelligent and structural features, which make their structure more complicated. This increasing structural complexity leads to the emergence of various defects during the manufacturing process, thereby necessitating the implementation of precise defect detection methods within automated production systems. In response to this challenge, we have developed an intelligent detection system aimed at identifying defects on seat surfaces and proposed the Dynamic Defect Detection (D<sup>3</sup>-Net) model as the algorithm for this system. The D<sup>3</sup>-Net model integrates a Dynamic Position-Aware Network (DPAN) to effectively capture multi-scale global and local information. Additionally, the Multi-Dimensional Adaptive Interaction Module (MDAIM) provides depth supervision aimed at minimizing background interference. To mitigate the effects of multifaceted materials on defect extraction, the Dual-Stream Dynamic Attention Fusion Module (D2AFM) consolidates feature information across various hierarchical levels. Utilizing our self-constructed dataset for leather seating surface defects, we demonstrate that D<sup>3</sup>-Net surpasses the Transformer-based DDQ-DETR model by 3.1% in Average Precision (AP). Further validation with a fabric seating surface defect dataset reveals a 3.2% improvement in AP over the Transformer-based DINO model, thereby confirming D<sup>3</sup>-Net's robust generalization capability. This model effectively extracts defect features and minimizes the influence of multifaceted backgrounds.</p>

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D3-Net: Dynamic Defect Detection Network for Identifying Defects in Multifaceted Material of Automotive Seats

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Rui Sun,
  • Dejin Zhao,
  • Xiaolong Yuan,
  • Yunjie Ma,
  • Jingzhe Zhang,
  • Xu Zhu,
  • Yi Xing,
  • Jialin Li,
  • Bo Li,
  • Dexin Kong,
  • Jianhai Zhang,
  • Xiangliang Kong

摘要

Automobile seats not only fulfill the traditional functions of comfort and safety but also increasingly incorporate various intelligent and structural features, which make their structure more complicated. This increasing structural complexity leads to the emergence of various defects during the manufacturing process, thereby necessitating the implementation of precise defect detection methods within automated production systems. In response to this challenge, we have developed an intelligent detection system aimed at identifying defects on seat surfaces and proposed the Dynamic Defect Detection (D3-Net) model as the algorithm for this system. The D3-Net model integrates a Dynamic Position-Aware Network (DPAN) to effectively capture multi-scale global and local information. Additionally, the Multi-Dimensional Adaptive Interaction Module (MDAIM) provides depth supervision aimed at minimizing background interference. To mitigate the effects of multifaceted materials on defect extraction, the Dual-Stream Dynamic Attention Fusion Module (D2AFM) consolidates feature information across various hierarchical levels. Utilizing our self-constructed dataset for leather seating surface defects, we demonstrate that D3-Net surpasses the Transformer-based DDQ-DETR model by 3.1% in Average Precision (AP). Further validation with a fabric seating surface defect dataset reveals a 3.2% improvement in AP over the Transformer-based DINO model, thereby confirming D3-Net's robust generalization capability. This model effectively extracts defect features and minimizes the influence of multifaceted backgrounds.