Detection of defects on the end face of small motor bearings based on machine vision

This paper focuses on the detection and identification of defects on the end faces of small motor bearings. Bearing defects significantly impact the performance and lifespan of motors, and traditional manual inspection methods suffer from low efficiency and high miss rates. This paper proposes the use of machine vision and image processing techniques to enhance the speed, efficiency, and reliability of detection. The study includes the analysis of common defect types on bearing end faces, the selection of industrial cameras and vision lighting, as well as image preprocessing and edge detection. By improving the YOLOv5 algorithm, the detection performance for small and medium-sized targets on bearing end faces is optimized. Moreover, a supervisory software for bearing defect detection has been designed to achieve more effective processing and statistical analysis of detection results. The primary goal of the research is to enhance the accuracy and automation level of detection, reduce the risk of omissions in manual inspections, and improve detection efficiency through the advancement of deep learning-based target detection algorithms. This research holds significant importance for the technological progress and production efficiency improvement in the small motor bearing industry.