An Effective Multi-Scale Feature Network for Detecting Connector Solder Joint Defects

Machines

Haikuo Shen¹  Kaihua Zhang² 

[1] Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Ministry of Education, Beijing 100044, China;School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Ministry of Education, Beijing 100044, China;

关键词: connectors;    solder joint defect;    multi-level feature fusion;    feature enhancement;   

DOI  :  10.3390/machines10020094

来源: DOAJ

【 摘 要 】

With the rapid development of industry, people’s requirements for the functionality, stability, and safety of electronic products are becoming higher and higher. As an important medium for power supply and information transmission functions of electronic products, high-quality soldering of cables and connectors ensures that the devices can operate normally. In this paper, we propose a multi-level feature detection network based on multi-level feature maps fusion and feature enhancement for detecting connector solder joints, classifying and locating qualified solder joints, and detecting seven common defective solder joints. This paper proposes a new feature map up-sampling algorithm and introduces a feature enhancement module, which better preserves the semantic information of higher-level feature maps, while at the same time enhancing the fused feature maps and weakening the effect of noise. Through comparison experiments, the mAP of the network proposed in this paper reaches 0.929 and the top-1 accuracy reaches 92%. The detection capability of each type of solder joint is greatly improved compared with the effect of other networks, which can assist engineers in the detection of weld joint quality and thus reduce the workload.

【 授权许可】

Unknown