Printed Circuit Board Quality Detection Method Integrating Lightweight Network and Dual Attention Mechanism

• Published in: IEEE access Vol. 10; pp. 87617 - 87629
• Main Authors: Wu, Ligang, Zhang, Liang, Zhou, Qian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Circuit boards; Convolutional neural networks; Deep learning; defect detection; Defects; dual attention mechanism; Fault detection; Feature extraction; Floating point arithmetic; Integrated circuit modeling; Lightweight; Machine learning; Mathematical models; Modules; Object detection; Parameters; printed circuit board; Printed circuit boards; Printed circuits; Real-time systems; Solid modeling; Weight reduction
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3198994

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 <inline-formula> <tex-math notation="LaTeX">89.4~FPS </tex-math></inline-formula> 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.