基于改进PP-YOLOv2的IC引脚焊接缺陷检测算法研究

doi:10.3969/j.issn.1000-1158.2023.10.13

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Abstract Aiming at the low detection accuracy of IC pin soldering defects due to small target size and dense pins, an algorithm for IC pin soldering defects detection based on improved PP-YOLOv2 is proposed. By introducing SE attention mechanism behind the backbone network, the importance of different channels in the feature map is distinguished, the key features of the target area are strengthened, and the network feature extraction ability is improved. k-means++ clustering algorithm is used to generate 9 cluster centers to reduce the error impact on the detection results caused by improper random selection of initial cluster centers. The experimental results show that the average accuracy of the improved algorithm for detecting the defects of IC pin soldering short circuit, missing pin, warping pin and little tin is 97.9%, 96.1%, 96.7% and 95.8% respectively. Under the threshold value of 0.5, the average accuracy reaches 96.6%, which is 14.9% and 5.1% higher than YOLOv3 and PP-YOLOv2 respectively. The detection time of the improved algorithm for a single picture is 0.151s, which meets the speed requirements of IC quality inspection and provides a reference value for IC pin soldering defect detection.

Key words： metrology soldering defect detection IC pin improved PP-YOLOv2 algorithm SE attention mechanism k-means++ machine vision

Received: 26 October 2022 Published: 10 October 2023

PACS:	TB973
	TB96

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	Articles by authors
	LI Na
	WANG Xue-ying
	HU Xiao-feng
	GUO Bin
	LUO Zai

Cite this article:

LI Na,WANG Xue-ying,HU Xiao-feng, et al. Research on IC Pin soldering Defect Detection Algorithm Based on Improved PP-YOLOv2[J]. Acta Metrologica Sinica, 2023, 44(10): 1574-1581.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.10.13 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I10/1574

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