ArticleInsects2023
Feature Refinement Method Based on the Two-Stage Detection Framework for Similar Pest Detection in the Field.
Article in Insects, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases.Journal of Zhejiang University. Science. B · 2026Article
- AMS-YOLO: multi-scale feature integration for intelligent plant protection against maize pests.Frontiers in plant science · 2025Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Efficient pest identification and control is critical for ensuring food safety. Therefore, automatic detection of pests has high practical value for Integrated Pest Management (IPM). However, complex field environments and the similarity in appearance among pests can pose a significant challenge to the accurate identification of pests. In this paper, a feature refinement method designed for similar pest detection in the field based on the two-stage detection framework is proposed. Firstly, we designed a context feature enhancement module to enhance the feature expression ability of the network for different pests. Secondly, the adaptive feature fusion network was proposed to avoid the suboptimal problem of feature selection on a single scale. Finally, we designed a novel task separation network with different fusion features constructed for the classification task and the localization task. Our method was evaluated on the proposed dataset of similar pests named SimilarPest5 and achieved a mean average precision (mAP) of 72.7%, which was better than other advanced object detection methods.
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Registered trials
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