Research on Defect Detection of Elevator Traction Steel Belt Based on YOLOv5

In order to achieve real-time detection of defects in the moving traction steel belt, a data set of traction steel cables was established and the improved YOLOv5 model was used for migration training. Image acquisition of defective samples in an environment that simulates usage scenarios. The video image data is processed, the position and category of defects are marked, and the data set is packaged. Using the principle of transfer learning, the improved YOLOv5 model is used to train on a large data set first, and then the trained model is applied to the target data set, so as to achieve anti-interference ability against complex backgrounds and maintain real-time detection.