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Densely packed object detection with transformer-based head and EM-merger

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Abstract

Due to the high density of objects and their varying sizes, detecting them accurately and without repetition in such scenarios is more challenging than traditional object detection methods. In this paper, we propose a YOLOv5-based object detection approach equipped with a Transformer-based Head and EM-Merger unit specifically designed for densely packed scenes. We incorporate the transformer architecture into the prediction heads to enable a self-attention mechanism that captures long-term dependencies between the densely packed objects. Additionally, we introduce an EM-Merger unit to resolve redundant object detections. Experimental results on the RebarDSC and SKU110K datasets demonstrate that our method significantly outperforms the baseline approach, achieving new state-of-the-art detection performance.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) 62272172, Guangdong Basic and Applied Basic Research Foundation 2023A1515012920, Tip-top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program 2019TQ05X200 and 2022 Tencent Wechat Rhino-Bird Focused Research Program (Tencent WeChat RBFR2022008), and the Major Key Project of PCL under Grant PCL2021A09.

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Author notes

  1. Xiaojing Zhong and Ni Zhang have contributed equally to this work.

Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, China

    Xiaojing Zhong, Ni Zhang & Qingyao Wu

  2. Key Laboratory of Big Data and Intelligent Robot, Ministry of Education, Guangzhou, China

    Xiaojing Zhong & Ni Zhang

  3. Pazhou Lab, Guangzhou, China

    Qingyao Wu

  4. Peng Cheng Laboratory, Guangzhou, China

    Qingyao Wu

  5. Zhongnan Building Materials Group Co. Ltd., Guangdong GW, Guangzhou, China

    Hao Hu

  6. Internet Technology Co. Ltd., Guangdong GW, Guangzhou, China

    Li Li & Junhua Cen

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  1. Xiaojing Zhong
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  2. Ni Zhang
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  3. Hao Hu
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  4. Li Li
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  5. Junhua Cen
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  6. Qingyao Wu
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Correspondence to Qingyao Wu.

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Zhong, X., Zhang, N., Hu, H. et al. Densely packed object detection with transformer-based head and EM-merger. SOCA 17, 109–117 (2023). https://doi.org/10.1007/s11761-023-00361-z

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  • DOI: https://doi.org/10.1007/s11761-023-00361-z

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