基于改进YOLOv5的钛丝表面缺陷检测算法OA

Titanium wire surface defect detection has important application value in industrial production.Compared with tra-ditional industrial images,titanium wire surface defect has the characteristics of small target,changeable shape and complex back-ground interference.Aiming at the difficulties of feature extraction,lack of detection accuracy,error detection and missing detec-tion of small and medium-sized targets in titanium wire surface defect detection,an improved YOLOv5s based target detection algo-rithm YOLOv5s-SCF is proposed.StarNet architecture is adopted as the backbone network of YOLOv5s,which can effectively re-duce the complexity of the model while maintaining the detection accuracy.The context-anchored attention mechanism(CAA)is in-troduced to supplement the multi-scale local features and enhance the feature extraction ability of the central region of the object.Using Focal-EIoU frame loss function,it focuses on high-quality anchor frames during regression,improves convergence speed and positioning accuracy.The experimental results show that compared with the original YOLOv5s algorithm,the improved model has a 2.1%improvement in mAP@0.5,which is suitable for real-time surface defect detection of industrial titanium wires.