Work

Printed Circuit Board Defect Detection Based on Lightweight Deep Learning Fusion Model

Year: 2025

Type: article

Abstract: Printed circuit boards (PCBs) are ubiquitous and essential electronic components. Tiny targets and high precision are the focus of PCB defect detection. This paper proposes an improved model focusing on tiny defect detection and model compression to achieve better performance in PCB defect detection. The improved model has a compact structure based on MobileNet v3 Small-CA and an image-cutting layer. Moreover, it applies an improved multi-scale fusion step with a location weighted mechanism to enhance representation performance. The proposed model outperforms state-of-the-art detection algorithms such as Faster R-CNN, EfficientDet, SSD, and YOLO v7, based on experimental results on a public synthetic PCB dataset. The proposed tiny object detection model has better performances on speed and detection accuracy, thereby benefitting the manufacturing industries associated with PCBs.

Source: Sensors

DOI: 10.3390/s25247403

Authors: Yuling Wang, Zhicheng Chen, Jie Wang, Peng Shang, Arcot Sowmya, Changming Sun

Institutions: East China University of Technology, UNSW Sydney, Commonwealth Scientific and Industrial Research Organisation, Data61

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137