DEC-YOLO: Surface Defect Detection Algorithm for Laser Nozzles

Aiming at the problems of misdetection, leakage, and low recognition accuracy caused by numerous surface defects and complex backgrounds of laser nozzles, this paper proposes DEC-YOLO, a novel detection model centered on the DEC Module (DenseNet-explicit visual center composite module). The DEC Module, as the core innovation, combines the dense connectivity of DenseNet with the local–global feature integration capability of the explicit visual center (EVC) to enhance gradient propagation stability during the training process and enhance fundamental defect feature extraction. To further optimize detection performance, three auxiliary strategies are introduced: (1) a head decoupling strategy to separate classification and regression tasks, (2) cross-layer connections for multi-scale feature fusion, and (3) coordinate attention to suppress background interference. The experimental results on a custom dataset demonstrate that DEC-YOLO achieves a mean average precision ([email protected]) of 87.5%, surpassing that of YOLOv7 by 10.5%, and meets the accuracy and speed requirements needed in the laser cutting production environment.