Machine Vision Inspection of Electrical Connectors Based on Improved Yolo v3

Weihao Wu,Qing Li

doi:10.1109/access.2020.3022405

Weihao Wu, Qing Li

Open Access

https://doi.org/10.1109/access.2020.3022405

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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.

Highlights

The electrical connector is a very important part in the overall aerospace industry, which plays the role of power connection and data connection [1], Because of its sealing strength, high bending strength, good air tightness and other characteristics, it plays a pivotal role in connection and packaging of aerospace, household appliances and other fields [2]
It can be seen that compared with the original Yolo v3 network, the improved Yolo v3 network improved the average accuracy by different degree in detecting defects 1 and 2, and the AP value of solder point defect detection increased from 85.6% to 92.5%
The improved algorithm in this paper is mainly based on the Yolo v3 network whose main improvement points are the selection of anchor boxes, the cluster analysis of self-made data sets, and the calculation of the suitable number and size of candidate boxes for this paper

Summary

INTRODUCTION

The electrical connector is a very important part in the overall aerospace industry, which plays the role of power connection and data connection [1], Because of its sealing strength, high bending strength, good air tightness and other characteristics, it plays a pivotal role in connection and packaging of aerospace, household appliances and other fields [2]. There are few researches on defect detection of multi-model electrical connectors, and traditional image processing methods are often used for detection, which has problems such as low accuracy, poor real-time performance, and poor robustness. Ground line detection can be achieved by traditional image processing technology, but traditional image processing technology focuses on the extraction of features, For slightly complex scene with more diversified features, it cannot extract the features very well so it can only be used in a relatively simple background and scenarios with low real-time requirements This method cannot meet the needs of this topic, but the popular deep learning network model in recent years can solve this problem very well. IMPROVED YOLO v3 The small defects existing on the circular electrical connector is the research object of this paper, and the detection of small targets is the main inspection task. The improvement of Yolo v3 is mainly in the clustering method and the network Structural modification in this article

ANCHOR BOXES CLUSTER ANALYSIS

MIXED DEFECT DETECTION EXPERIMENT

Findings

CONCLUSION