Improved YOLOv5 based on deformable convolution and efficient decoupled head for pill surface defect detection

Kang Liu; Zhongliang Lv; Kewen Xia; Chuande Zhou; Zhenyu Lu; Hailun Zuo; Zelun Li; Xuanlin Chen

doi:10.1117/12.2688970

6 November 2023 Improved YOLOv5 based on deformable convolution and efficient decoupled head for pill surface defect detection

Kang Liu, Zhongliang Lv, Kewen Xia, Chuande Zhou, Zhenyu Lu, Hailun Zuo, Zelun Li, Xuanlin Chen

Author Affiliations +

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 1292123 (2023) https://doi.org/10.1117/12.2688970
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

Aiming at the problems of low detection efficiency and poor detection accuracy caused by different shapes and sizes of pill defects in the existing market, an improved YOLOv5s model is proposed. In this study, deformable convolution is introduced into CBS structure and combined with C3 module to enable the model to adaptively adjust the receptive field and more effectively approach the shape and size of the defect target. In addition, the Efficient Decoupled Head (EDH) is introduced to replace the YOLOv5s detection head, separating the positioning task and the classification task, and improving the detection accuracy and target coverage. The experiment on the pill dataset shows that the model proposed in this paper effectively improved the detection accuracy of pill defects. The model improved the accuracy of round crack and elliptical crack defects with large differences in size and shape by 1.9% and 3.0%, respectively. Compared with YOLOv5s, mAP@0.5 and mAP@0.5:0.95 reached 97.7% and 72.3%, respectively, which increased by 1.2% and 2.6%. Additionally, the model has a smaller model volume and faster inference speed.

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Kang Liu, Zhongliang Lv, Kewen Xia, Chuande Zhou, Zhenyu Lu, Hailun Zuo, Zelun Li, and Xuanlin Chen "Improved YOLOv5 based on deformable convolution and efficient decoupled head for pill surface defect detection", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 1292123 (6 November 2023); https://doi.org/10.1117/12.2688970

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KEYWORDS

Convolution

Detection and tracking algorithms

Deformation

Defect detection

Performance modeling

Target detection

Contamination

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