The PCB surface defect detection system based on GPU acceleration

摘要

Defective products are unavoidable in printed circuit board production process, so rapid detection and identification methods are badly in need of. PCB surface defect detection including a series of processing such as surface image capture, mixed noise filtering, images registering and so on, so it takes a lot of CPU time. To improve detection speed, based on GPU parallel computing platform, we designed a reasonable parallel processing system for PCB defect detection to meet the need of real-time requirements of a production line. Experimental results show that parallel image processing algorithms based on GPU can achieve good results compared to the CPU-based serial algorithm (with speed up ratio up to 8.34 in this paper), providing a new approach for rapid detection of PCB surface defect.