Scientific Reports (Jun 2023)

Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection

  • Kewen Xia,
  • Zhongliang Lv,
  • Kang Liu,
  • Zhenyu Lu,
  • Chuande Zhou,
  • Hong Zhu,
  • Xuanlin Chen

DOI
https://doi.org/10.1038/s41598-023-36854-2
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 16

Abstract

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Abstract To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, [email protected], and [email protected]:0.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms.