Printed Circuit Board Defect Detection Using YOLOv8s and TensorRT

Printed Circuit Boards (PCBs) are vital in modern manufacturing, acting as essential electronic device components and contributing to various technologies' seamless functioning. Due to their significant impact on device performance, any notable flaws in PCBs can lead to operational issues and potential safety hazards. This emphasizes the need for effective defect detection and quality control measures in this crucial electronic component. This research aims to tackle the challenges associated with identifying defects in PCBs by employing deep learning methods. Defects are categorized into functional and cosmetic types, with sub-datasets covering common issues such as missing holes, mouse bites, open circuits, short circuits, spur issues, and spurious copper. Recognizing the variations in PCB designs and the limited availability of defect samples, the project utilizes the You Only Look Once (YOLO) model, which is widely used in object detection. The objective is to train the model to accurately classify defects within the PCB dataset, integrating TensorRT to improve inference program time. The proposed YOLOv8s-TensorRT model had a mean average precision 50 (mAP50) score of 98.5%, which is only 0.1 % lower than the YOLOv8s. Despite that, the integration of TensorRT reduced inference time by 0.9ms and lowered the model size by about 39.3%, both of which are significant in real-time object detection.