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An efficient network for multi-scale and overlapped wildlife detection

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Abstract

Wildlife protection is crucial to the earth's ecological civilization construction. With the use of computer vision technology, we can efficiently and accurately monitor wildlife. In this paper, a wildlife database is built and a YOLO for wildlife detection (WD-YOLO) is proposed. Firstly, we design a Weighted Path Aggregation Network for the detection of multi-scale wild animals, which fuses the hierarchical feature maps extracted by the backbone network. Secondly, a Neighborhood Analysis Non-Maximum Suppression is proposed to solve the problem of multi-targets overlap in wildlife detection. The prediction results of adjacent animals are retained and the redundant detection boxes are eliminated effectively when performing suppression operations. Finally, in order to improve the generalization performance of the network, CutOut and MixUp are used for image augmentation, so that the model can adapt to different scenarios. Experimental results show that the precision of wildlife detection has improved by 5.543% and the recall has improved by 1.651% compared with the pre-optimized model. The proposed WD-YOLO significantly outperforms the other state-of-the-art one-stage object detection networks in our self-built wildlife dataset.

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Acknowledgements

This work was supported by the Key Research and Development Program in Jiangsu Province (No. BE2016739).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Xin Lu & Xiaobo Lu

  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China

    Xin Lu & Xiaobo Lu

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  1. Xin Lu
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  2. Xiaobo Lu
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Correspondence to Xiaobo Lu.

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Lu, X., Lu, X. An efficient network for multi-scale and overlapped wildlife detection. SIViP 17, 343–351 (2023). https://doi.org/10.1007/s11760-022-02237-9

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  • DOI: https://doi.org/10.1007/s11760-022-02237-9

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