Defect Detection in Printed Circuit Boards Based on EdgeML and Computer Vision

The production of electronic boards is a common activity in the industrial environment, and ensuring their quality is essential for obtaining reliable products. This work presents a comparative study of the performance of three machine learning architectures: YoloV8, FOMO, and MobileNet. The dataset was created based on four classes for assembly fault detection. The model that achieved the best results was FOMO, with precision, recall, and Fl-Score above 95 %, as well as processing 10.5 frames per second and having a total size of 152 kB.