Multi defect detection of crystal oscillators based on improved Yolov5

DOI:	10.11809/bqzbgcxb2023.07.004
Keywords:	crystal oscillators; multi defect; deep learning; hardware acceleration; multi defect detection
Abstract:	The detection of defects in crystal oscillators has mainly relied on manual work, which has the problems of low efficiency, high degrees of external influence and high cost. Traditional methods are unable to detect multiple defects in a single record, while deep learning application in the industry also suffers from slow inference due to a lack of GPU acceleration. To address these issues, the algorithm model is selected to improve the detection in various ways, including conduction of image enhancement in the input module, feature map fusion of the backbone network, introduction of a new scale detection layer and new activation functions into the neck network, and addition of the attention mechanism into the output prediction section. These efforts increase the tiny target detection capability while retaining the lightweight of the model. Finally, model pruning and hardware accelerated inference are carried out, and the research is deployed on a PC to perform defect statistics and other tasks. The experiments show that the detection rate of the system is above 95%, realizing the automation and refinement of defect detection of crystal oscillators.
Published:	2023-07-28
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