Industrial defect target detection based on YoloV5 and attention mechanism

Abstract

Crack detection of industrial defect target detection is one of the most critical aspects of industrial product quality control, and to address the problems of false detection, missed detection and insufficient feature extraction for fine cracks in target detection, this paper introduces a hybrid attention mechanism based on the original YOLOv5, which improves the accuracy of the backbone feature extraction network for fine crack detection. The experimental results show that the target loss of the validation set of the improved YOLOv5s model converges significantly, the model training results are accurate, there is no overfitting or underfitting phenomenon, and the average accuracy mean value is improved by 3.8% compared with the original YOLOv5s model. The improved YOLOv5s model can identify and detect fine cracks under both illumination or dim conditions, and the model generalization ability is good enough to meet the relevant requirements in industrial production processes.