Experimental design of PCB defect detection system based on ZYNQ

The detection method of printed circuit board (PCB) is crucial to ensure the normal operation of the product. In view of the problems that traditional manual detection methods are prone to miss detection and false detection, this paper uses the deep learning method to detect PCB defects, and builds a hardware implementation platform based on ZYNQ. It uses the software and hardware co-design method to accelerate the algorithm using FPGA. Among them, YOLOv3-SPP network model is adopted, and the structure is optimized to make it suitable for the deployment of ZYNQ terminal. When building the hardware platform, first configure the basic hardware information through Vivado, then use PetaLinux to create a Linux system, call the system in Vitis and add the DPU IP core, and finally write Python programs on the PS side of ZYNQ with the idea of multithreading to achieve PCB defect detection. The experimental results show that the detection accuracy of the system for various types of PCB defects is above 0.95, and the average detection accuracy (mAP) is 0.97.