DAE-YOLO:An Improved Industrial Surface Defect Detection Algorithm Based on YOLOv11

Abstract

Accurate identification of defects in industrial products is fundamental to maintaining high-quality manufacturing standards. However, due to the high proportion of small defects and the significant cross-scale characteristics present on industrial surfaces, achieving high-precision detection becomes exceptionally challenging. This study proposes a high-precision neural network model, DAE-YOLO, built upon YOLOv11, for surface defect detection in industrial products.

To address the prominent issue of cross-scale characteristics, an efficient multi-scale attention mechanism (EMA) was integrated into the backbone network, resulting in the development of a novel module, D-EMA. 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) 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.