Design and research of chip vision positioning system

Lei Liu; Changkun Wang; Qing Li

doi:10.1117/12.2660024

3 February 2023 Design and research of chip vision positioning system

Lei Liu, Changkun Wang, Qing Li

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111A (2023) https://doi.org/10.1117/12.2660024
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Thinking of the low accuracy of chip detection and location during the chip testing, this paper proposes an optimized YoloV2 model for chip detection and location in the process of chip testing. Firstly, this paper directly builds the chip image acquisition platform to collect the image data set. Because the image data collected in the experiment is less, this paper uses 5 different image transformations to expand the data set, which solves the problems of over fitting, low universality and weak robustness caused by small data set. Secondly, this paper uses the optimized MobileNetV2 model to replace the original Darknet19 model of YoloV2, which reduces the amount of parameters and enhances the recognition speed of the network. Finally, this paper modifies the regression calculation method of the bounding box of the original YoloV2 model to accelerate the regression speed and accuracy of the bounding box. The experimental results show that the parameter size of optimized YoloV2 model is just only 82.3MB, and the recognition accuracy is better than YoloV2 model and template matching algorithm. It can effectively work out the problems of missing inspection, false inspection and poor accuracy in chip positioning in chip testing.

Citation Download Citation

Lei Liu, Changkun Wang, and Qing Li "Design and research of chip vision positioning system", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111A (3 February 2023); https://doi.org/10.1117/12.2660024

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