Research on debris detection technology for aerospace electrical connectors

Quality inspection of aerospace electrical connectors is one of the crucial safeguards for ensuring the safety of aerospace systems. However, the traditional method of manual sampling inspection struggles to meet the increasing production demands and carries the risk of undetected issues. This paper investigates the debris detection technology for aerospace electrical connectors based on machine vision and develops an automated vision inspection system to replace manual inspection for complex debris detection tasks in aerospace electrical connectors. In the study, based on the recognized pin points and same sequence pin alignment, the pin and the background of the printed line for the detection of debris are shielded, using the mask method and RANSAC algorithm, and the suspected debris region is extracted. Subsequently, the detection accuracy of the residual detection models obtained by different methods is compared, and the best-performing model, ShuffleNet-V2, is optimized. In the final test, the model trained by improved ShuffleNet-V2 network with the attention module ECA has the best detection effect with an accuracy of 99.2%.