Author:
Xia Kewen,Lv Zhongliang,Liu Kang,Lu Zhenyu,Zhou Chuande,Zhu Hong,Chen Xuanlin
Abstract
AbstractTo 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, mAP@0.5, and mAP@0.5:0.95 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.
Funder
Innovation Program for Master Students of Chongqing University of Science and Technology
National Natural Science Foundation of China
Chongqing Talents Program Innovation and Entrepreneurship Demonstration Team
Chongqing Research Program of Basic Research and Frontier Technology
Science and Technology Research Program of Chongqing Municipal Education Commission
Publisher
Springer Science and Business Media LLC
Cited by
16 articles.
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