Open Access
Printed Circuit Board Quality Detection Method Integrating Lightweight Network and Dual Attention Mechanism
Author(s) -
Ligang Wu,
Liang Zhang,
Qian Zhou
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3198994
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Printed circuit boards are versatile and highly printed, which can be widely used in various fields, and also provide new opportunities for the development of electronic information equipment. However, it is difficult to detect defects and faults during the production and use of printed circuit boards. In this paper, in view of the defects and fault detection of printed circuit boards, a deep learning detection method GSC YOLOv5 that integrates light-weight network and dual attention mechanism is proposed. First, GSC YOLOv5 is improved on the basis of YOLOv5. Ghost Conv and Ghost Bottleneck are used to realize the lightweight of the algorithm structure, reduce the number of parameters and floating point arithmetic (Flops) of the model. Second, the dual attention mechanism of Squeeze-and-Excitation Module (SE Module) and Convolutional Block Attention Module (CBAM) are introduced to optimize the performance of the algorithm, while improves the detection precision and real-time detection efficiency. Last, the experimental results show that compared with the original algorithm, GSC YOLOv5 reduces the amount of parameters and Flops by 50.38% and 55.52%, respectively, and compresses the model volume by 50.26%. Furthermore, the detection precision is increased by 2.41% and the recall rate is improved by 1.06%. At the same time, a real-times detection performance of $89.4~FPS$ is achieved, it improves by 65.18%. Therefore, the proposed algorithm is not only lightweight but also can achieve better performance, it can satisfy the detection requirements of printed circuit board.