Application of neural networks for control of printed circuit boards using 3D x-ray microtomography data

The article discusses a method for detecting PCB defects using neural networks. The analysis of various neural network architectures is carried out to identify the most effective. An approach to filtering data simulating the operation of a microtomograph using convolutional autoencoders is also presented. To assess the quality of the proposed approaches, the mean Average Precision (mAP) metric for the YOLOv8 and Faster R-CNN models was used.