Machine Vision Inspection of Electrical Connectors Based on Improved Yolo v3

Abstract

Aiming at the problems of electrical connector defect detection, such as low automation, low detection accuracy, slow detection speed, and poor robustness, an improved Yolo v3 algorithm was proposed in this paper to detect electrical connector defects. First, the K-means clustering algorithm is used to perform cluster analysis on the data set of this paper to obtain three kinds of candidate frames with aspect ratios, aiming at improving the detection accuracy for the defective objects in this paper; the 8-fold downsampled feature map outputted by the third residual block of the backbone network is upsampled 4 times, and the obtained feature map is merged with the 2-fold downsampled feature map outputted by the second residual block to obtain the fusion feature detection layer; the 6 DBL units passed by the target detection layer are changed to 2 DBL unit plus 2 residual units to improve feature reuse and acquisition; In addition, single-scale feature maps are chose for target detection in this paper instead of multi-scale prediction of the original network, which not only saves the calculation amount, but also avoids false detection to a certain extent.; A new detection method is proposed for relative rotation defects between the inner ring area and the outer ring area of the electrical connector. The qualitative and quantitative experimental results show that the improved Yolo v3 algorithm in this paper has better performance and speed for defect detection of various types of electrical connectors, with an accuracy rate of 93.5%, which is more accurate than Faster R-CNN. The original Yolo v3 is faster and basically meets the requirements of the industrial field for electrical connector testing.

Keywords

• Defect etection
• electrical connector
• machine vision
• deep learning
• Yolo v3