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<p>Although texture features inherently contain rich contextual and structural information, they have rarely been explicitly considered in mainstream object detection tasks for surface defect identification. In industrial scenarios, low contrast between defects and complex background textures severely impedes accurate localization and recognition, leading to inadequate modeling of multi-scale textures and fine-grained details, and ultimately degrading detection accuracy. To address these challenges, we propose a novel Deep Texture Exploration and Multi-Scale Interaction Network (TexMS-YOLO), specifically designed to enhance texture-aware and scale-sensitive defect detection. First, we develop the Texture-Aware Feature Fusion (TAFF) module, which fuses high- and low-resolution features across spatial and frequency domains. Through adaptive parameter optimization, TAFF efficiently extracts discriminative texture representations at multiple scales, thereby enhancing the model’s sensitivity to texture-related defect features. Additionally, a learnable high-pass filtering mechanism is introduced to preserve edge structures and fine detail variations essential for identifying subtle defects. Furthermore, we introduce a Multi-Scale Interaction (MSI) module that employs gated units and spatially adaptive structures to dynamically select scale-relevant features. A bottom-up pathway aggregates local and global cues along channels, effectively enhancing the detection of scale-variant defects. Lastly, to mitigate semantic inconsistency in traditional pyramidal frameworks, we adopt the Triple Feature Encoding (TFE) module, which applies dual-modal pooling, zeroth-order interpolation, and dynamic weighting for enhanced multi-scale representation. Extensive experiments on a self-collected relay surface defect dataset and the public NEU-DET benchmark demonstrate that TexMS-YOLO significantly outperforms existing state-of-the-art detection methods across multiple evaluation metrics, confirming its robustness and practical effectiveness in real-world industrial applications.</p>

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Deep texture exploration and multi-scale interaction network for surface defect detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Yuxin Ma,
  • Enrui Gong,
  • Feng Huang

摘要

Although texture features inherently contain rich contextual and structural information, they have rarely been explicitly considered in mainstream object detection tasks for surface defect identification. In industrial scenarios, low contrast between defects and complex background textures severely impedes accurate localization and recognition, leading to inadequate modeling of multi-scale textures and fine-grained details, and ultimately degrading detection accuracy. To address these challenges, we propose a novel Deep Texture Exploration and Multi-Scale Interaction Network (TexMS-YOLO), specifically designed to enhance texture-aware and scale-sensitive defect detection. First, we develop the Texture-Aware Feature Fusion (TAFF) module, which fuses high- and low-resolution features across spatial and frequency domains. Through adaptive parameter optimization, TAFF efficiently extracts discriminative texture representations at multiple scales, thereby enhancing the model’s sensitivity to texture-related defect features. Additionally, a learnable high-pass filtering mechanism is introduced to preserve edge structures and fine detail variations essential for identifying subtle defects. Furthermore, we introduce a Multi-Scale Interaction (MSI) module that employs gated units and spatially adaptive structures to dynamically select scale-relevant features. A bottom-up pathway aggregates local and global cues along channels, effectively enhancing the detection of scale-variant defects. Lastly, to mitigate semantic inconsistency in traditional pyramidal frameworks, we adopt the Triple Feature Encoding (TFE) module, which applies dual-modal pooling, zeroth-order interpolation, and dynamic weighting for enhanced multi-scale representation. Extensive experiments on a self-collected relay surface defect dataset and the public NEU-DET benchmark demonstrate that TexMS-YOLO significantly outperforms existing state-of-the-art detection methods across multiple evaluation metrics, confirming its robustness and practical effectiveness in real-world industrial applications.