A lightweight and robust detection network for diverse glass surface defects via scale- and shape-aware feature extraction

• Published in: Engineering applications of artificial intelligence Vol. 153; p. 110640
• Main Authors: Yu, Huan, Wang, Jin, Yang, Jingru, Liang, Yiming, Li, Zhihui, Wang, Zhan, He, Haiyan, Zhang, Xusheng, Lu, Guodong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2025
• Subjects: Deformable convolution; Glass defect detection; Multi-scale attention; Surface defect detection; Surface defect detection; Multi-scale attention; Deformable convolution; Glass defect detection
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2025.110640

As glass usage expands across industries, intelligent glass defect detection is essential for ensuring quality. However, the varying shapes and sizes of defects, coupled with numerous subtle defects and the demand for efficient detection, present challenges for existing methods in achieving both accurate and real-time detection. To address these, we propose the lightweight and robust Glass Surface Defect Network (GSDNet) via scale- and shape-aware feature extraction. Specifically, the novel Shape-aware Feature Extraction (SFE) block, which employs deformable convolution with special linear shape-adaptive offset constraints, forms the feature extraction network, enabling the adaptive extraction of local features for defects with irregular shapes. Meanwhile, the Scale-aware (SA) attention is proposed, incorporating spatial attention mechanism to guide the model in focusing on key features across different receptive fields, enhancing defect detection at various scales. Finally, to enhance detection efficiency, the Efficient Bidirectional Path Aggregation Network (EBiPAN) is proposed as the feature aggregation module, integrating high-resolution information through bi-directional concatenation to improve small defect detection while avoiding significant additional computational burden. To validate the effectiveness of GSDNet, we compile the first multi-class glass defect dataset, covering 4 types of glass and 12 defect categories. Extensive experiments demonstrate GSDNet exhibits exceptional accuracy and robustness, consistently outperforming 9 advanced networks, with a 6.8% improvement in mean Average Precision and a notable 10.9% improvement in mean Average Precision small over the You Only Look Once version 8. Moreover, the optimal balance of accuracy and efficiency is achieved, with a detection speed of 68 frames per second. The dataset and code are publicly available at: https://github.com/FisherYuuri/GSDNet.