Abstract
The printed circuit board (PCB) is one of the important components in the electronic industry. Among them, Establishing Pre-reflow PCB defect detection before reflow soldering is critical to PCB production quality guarantee. More than a dozen different algorithms should be adopted to successively inspect various defects in different types of elements in existing Pre-reflow printed circuit board (PCB) defect detection systems. This may complicate the corresponding detection process as well as reduce the robustness of detection systems. To address the shortcomings of currently utilised detection systems, a deep learning-based object detection algorithm is combined with the traditional template matching approach to put forward an efficient Pre-reflow PCB defect detection method. The proposed method possesses a rather strong feature learning capability. By establishing the corresponding mathematical model, it can precisely identify a specific defect type, thus realising synchronous detection of elements and defects of diverse types. The improved CentreNet model has a detection speed of 102 FPS when targeting relevant datasets, and its detection precision index AP75 reaches 97.1, which is 0.6 higher than the original model. Subsequently, dichotomous and eight-category tests were performed to validate the proposed Pre-reflow PCB defect detection method. It turns out that the accuracy of this method is up to 98.6% in dichotomous tests but 95.3% in eight-category tests. Therefore, it is sufficiently proved by relevant experimental results that the proposed method has the capability of detecting different types of defects in a variety of elements synchronously.
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Yang, Y., Guo, H., Fu, X. (2023). On Pre-reflow PCB Defect Detection Based on Object Detection and Template Matching. In: Dong, J., Zhang, L. (eds) Proceedings of the International Conference on Internet of Things, Communication and Intelligent Technology . IoTCIT 2022. Lecture Notes in Electrical Engineering, vol 1015. Springer, Singapore. https://doi.org/10.1007/978-981-99-0416-7_63
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DOI: https://doi.org/10.1007/978-981-99-0416-7_63
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