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OSAF-Net: A one-stage anchor-free detector for small-target crop pest detection

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Abstract

Multi-class crop pest detection in massive images is a practically challenging problem. Recently, convolutional neural networks (CNN) based approaches have shown promise in detecting crop pests, but there are still significant obstacles to overcome. Two primary challenges include the highly similar physical appearance of some categories, making it difficult to distinguish the specific categories manually, and the multi-scale characteristics of pest objects, leading to numerous false negative detections, especially for small pests. To address the above problems, we propose a one-stage anchor-free detection network (OSAF-Net) with strong performance and robustness. Firstly, a dynamic training sample selection (DTSS) method is devised to capture high-quality training examples that contain multi-scale contextual information to improve the detection performance of pests with diverse scales. Secondly, to mitigate the disturbance from the similar physical appearance of pests, a dynamic detection head (DDH) is introduced to accurately obtain more representative semantic features to locate and distinguish pest objects. The proposed DTSS and DDH methods are stable for implementation. They can be combined with existing state-of-the-art detection network architectures. Extensive experiments conducted on two datasets, CropPest24 and MPD2018, demonstrate that our proposed method has a competitive performance, achieving AP50 of 77.3% on CropPest24 and 81.3% on MPD2018.

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Data availability

We conduct extensive experiments on two datasets, croppest24 will be available soon, the public link is [28] and MPD2018 dataset is from the article [15].

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Funding

This work was supported in part by project of Dean’s Fund of Hefei Institute of Physical Science, Chinese Academy of Sciences (YZJJ2022QN32) and the major special project of Anhui Province Science and Technology (2020b06050001).

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Authors and Affiliations

  1. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Science, Hefei, 230031, China

    Rujing Wang, Shifeng Dong, Lin Jiao, Jianming Du & Ziliang Huang

  2. University of Science and Technology of China, Hefei, 230026, China

    Rujing Wang, Shifeng Dong & Ziliang Huang

  3. Anhui University, Hefei, 230031, Anhui, China

    Rujing Wang & Lin Jiao

  4. Southwest University of Science and Technology, Mianyang, 621000, China

    Shijian Zheng & Chenrui Kang

Authors
  1. Rujing Wang
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  2. Shifeng Dong
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  3. Lin Jiao
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  4. Jianming Du
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Correspondence to Shifeng Dong.

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Wang, R., Dong, S., Jiao, L. et al. OSAF-Net: A one-stage anchor-free detector for small-target crop pest detection. Appl Intell 53, 24895–24907 (2023). https://doi.org/10.1007/s10489-023-04862-4

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