Development of a real-time Printed Circuit board (PCB) visual inspection system using You Only Look Once (YOLO) and fuzzy logic algorithms

Article type: Research Article

Authors: Huo, Xiaoyan^{; *}

Affiliations: Information Construction and Management Center, Jiaozuo University, Jiaozuo, Henan, China

Correspondence: [*] Corresponding author. Xiaoyan Huo. E-mail: huoxiaoyan2004@163.com.

Abstract: Automated visual inspection on PCB boards is a critical process in electronic industries. Misalignment component detection is one of the challenging tasks in the PCB inspection process. Defects during the production process might include missing and misaligned components as well as poor solder connections. Inspection of PCB is therefore required to create practically defect-free products. There are various methods have been developed to perform this task in literature. The significance of this research is to propose an efficient with low-cost system is still require in small scale manufacturing to perform the misalignment or missing component detection on PCB boards. However, an efficient, low-cost system is still required in small-scale manufacturing to perform the misalignment or missing component detection on PCB boards. In this study, a real-time visual inspection system is developed for misalignment component detection. The proposed system consists of hardware and software frameworks. The hardware framework involves the setup of devices and modules. The software framework is composed of pre-processing and post-processing. In pre-processing, image enhancement is applied to remove noises from captured images and You Only Look Once (YOLO) object detector for components detection. Subsequently, the detected components are compared to the corresponding defined pattern using a template-matching algorithm. As experimental shown, the proposed system satisfies the requirement of missing component detection on PCB boards.

Keywords: Surface defect detection, visual inspection, PCB, YOLO, fuzzy logic

DOI: 10.3233/JIFS-223773

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4139-4145, 2023