Abstract

The accurate identification of surface defects in industrial products is critical for ensuring consistent high-quality standards in modern manufacturing. Nevertheless, the prevalence of small-scale defects and pronounced cross-scale variability on industrial surfaces poses substantial challenges to achieving precise detection. To address these issues, this study introduces a high-precision neural network architecture, termed DAE-YOLO, which is developed based on the YOLOv11 framework and specifically tailored for industrial surface defect detection. To effectively mitigate the impact of cross-scale feature discrepancies, an efficient multi-scale attention mechanism (EMA) is embedded within the backbone network. This leads to the design of a novel feature extraction module, D-EMA (Deformable and Efficient Multi-scale Attention), which enhances the model's ability to capture both global contextual dependencies and local structural details. This module enhances the feature extraction capabilities of the convolutional neural network by fusing global context and local features, thereby enabling precise modeling of spatial information and pixel-level relationships. The design of D-EMA carefully considers the fusion of cross-spatial information, allowing the model to effectively process targets at varying scales while preserving accurate location information, significantly improving performance in defect detection tasks. Further, to address the challenge of detecting small targets, the latest deformable convolutional module was incorporated into the Neck component. A new bidirectional weighted feature pyramid network (BDFPN, bidirectional deformable feature pyramid network) was then designed, combining multi-scale features to enhance the representation of small defect features. Experiments conducted on two publicly available datasets (NEU-DET and PCB defect) demonstrated the effectiveness of the proposed network architecture. The results indicate that the proposed method outperforms YOLOv11 by 3.88 % in detection accuracy on the NEU-DET dataset, and by 2.8 % on the PCB defect dataset.

Keywords

MSC

68U10

Cite this article

@article{cai-8174,
  author  = {Guangqiao Xie and Yan Zhou and Chengshang Wu},
  title   = {DAE-YOLO: An Improved Industrial Surface Defect Detection Algorithm Based on YOLOv11},
  journal = {Computing and Informatics},
  volume  = {45},
  number  = {5},
  pages   = {1100--1128},
  year    = {2026},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2026_5_1100}
}
Download .bib