Visual Detection Method for Defects in Bearing Parts Based on I-YOLO Model
Bearing is an important component of hydraulic dynamometer,and its quality affects the safe operation of the unit.The traditional machine vision based bearing defect detection method has the problems of low accuracy and high missed detection rate.A bearing defect detection method based on an improved YOLO model is proposed to address this issue.Firstly,image preprocessing is performed on the dataset to expand the training samples.Then,by improving the YOLO model structure,a new I-YOLO deep learning model is proposed through candidate box dimension clustering and multi-scale training.Finally,train and test the I-YOLO model on the bearing dataset,and compare it with the YOLOv4 and YOLOv3 models.The result shows that the improved I-YOLO model achieves an accuracy of 98.73%in bearing defect detection,which is 3.01%and 10.88%higher than the YOLOv4 and YOLOv3 models,respectively.This proves that the improved model can effectively reduce the missed detection rate while improving detection accuracy.
deep learningbearingsconvolutional neural modelsvisual recognitiondefect detection
万方数据
维普
