期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 168, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2019.105114
关键词
Stored-grain insect monitoring; Multi-scale detector; Feature pyramid network; Loss function
资金
- China Special Fund for Grain-Scientific Research in Public Interest [201513002]
- Program of Introducing Talents of Discipline to Universities of China [B08004]
In order to implement intelligent monitoring for insects in grain warehouses, a Multi-Scale Insect Detector (MSI_Detector) was developed by applying deep convolutional neural networks. It solved the problem existing in common anchor-based insect detection methods whose performance decrease sharply as insects become smaller. We built a feature pyramid network to extract insect image features with different spatial resolutions and semantic information, and tiled anchors with reasonable scales on each pyramid level to handle different scales of insects well. Besides, we altered the classification and box regression subnets to residual structure in order to improve the detection performance, and proposed the recombined loss function to balance the weights of easy and hard samples during training for both insect classification and box regression tasks. Excellent results for the detection of adults of 10 species 6 genera of common stored-grain insects were achieved, the mean Average Precision reached 94.77%, which demonstrated the robustness to insect scales.
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