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A global activated feature pyramid network for tiny pest detection in the wild

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Abstract

Small object detection techniques have been developed for decades, but one of key remaining open challenges is detecting tiny objects in wild or nature scenes. While recent works on deep learning techniques have shown a promising potential direction on common object detection in the wild, their accuracy and robustness on tiny object detection in the wild are still unsatisfactory. In this paper, we target at studying the problem of tiny pest detection in the wild and propose a new effective deep learning approach. It builds up a global activated feature pyramid network on convolutional neural network backbone for detecting tiny pests across a large range of scales over both positions and pyramid levels. The network enables retrieving the depth and spatial intension information over different levels in the feature pyramid. It makes variance or changes of spatial or depth-sensitive features in tiny pest images more visible. Besides, a hard example enhancement strategy is also proposed to implement fast and efficient training in this approach. The approach is evaluated on our newly built large-scale wide tiny pest dataset containing 27.8K images with 145.6K manually labelled pest objects. The results show that our approach perform well on pest detection with over 71% mAP, which outweighs other state-of-the-art object detection methods.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 31401293, and in part by the Major Special Science and Technology Project of Anhui Province under Grant 201903a06020006.

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Authors and Affiliations

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

    Liu Liu & Fangyuan Wang

  2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China

    Rujing Wang, Chengjun Xie & Rui Li

  3. South China Agricultural University, Guangzhou, 510642, China

    Long Qi

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  1. Liu Liu
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Correspondence to Rujing Wang.

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Liu, L., Wang, R., Xie, C. et al. A global activated feature pyramid network for tiny pest detection in the wild. Machine Vision and Applications 33, 76 (2022). https://doi.org/10.1007/s00138-022-01310-0

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