PCBs are very influential on the manufacture of electronic devices, for example when there is even a small number of PCB paths that are cut off or damaged, the electronic device cannot be operated properly. Therefore, in this study, the author tried to create and analyze a defect checking tool on PCBs to replace human vision to make it easier and can save costs. This tool is equipped with the help of a Logitech c920 Webcam and a Raspberry Pi 3b+ microprocessor which is used to store and run programs that have been created on Python programming software, so this tool can be used portablely. With these two technologies, Image Processing can be used to detect objects with the OpenCv library and Google Colab. PCB defect detection tool with the help of Image Processing uses yolo convolutional neural network method to help determine path damage on the PCB. You Only Look Once (YOLO) algorithm with five detection classifications, namely short, open circuit, missing hole, mouse bite, and spur. From the results of the study, the results were obtained that the YOLO algorithm was able to detect these five classifications with a value of mAP@0.5 short 90.67%, open circuit 97.86%, Mouse Bite 94.43%, Missing Hole 96.09%, and spur 97.56%.