GIWT-YOLO: an efficient multi-scale framework for real-time Scolytinae pests detection Article Swipe
Jingwei Liu
,
Yongke Li
,
Lei Wang
,
Yunjie Zhao
,
Bowen Mao
,
Pengying Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/finsc.2025.1635439
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/finsc.2025.1635439
The broad range of Scolytinae pests sizes and their subtle visual similarities, especially in smaller species, continue to challenge the accuracy of mainstream object detection models. To address these challenges, this paper proposes GIWT-YOLO, a lightweight detection model based on YOLOv11s, specifically tailored for Scolytinae pests detection. (1) We designed a lightweight multi-scale convolution module, GIConv, to improve the model’s ability to extract features at different pest scales. This module enhances the accuracy of small-object detection while reducing the computational cost and parameter complexity of the backbone. (2) The WTConv module inspired by wavelet transform is introduced into the backbone. This enlarges the effective receptive field and improves the model’s ability to distinguish pests with similar textures. (3) An SE attention mechanism is incorporated between the Neck and Head to enhance the model’s focus on key feature regions. Experimental results show that GIWT-YOLO achieves 84.7% in Precision, 88.7% in mAP@50, and 63.4% in mAP@50~95, which are improvements of 2.2%, 4.0%, and 3.1%, respectively, compared to the baseline YOLOv11s. Additionally, the model’s parameters and GFLOPs are reduced by 11.3% and 13.4%, respectively. Our proposed model surpasses the state-of-the-art (SOTA) performance in small-sized pest detection while maintaining a lightweight architecture, and its generalization ability has been validated on other public datasets. Our model provides an efficient solution for detecting Scolytinae pests. In future work, we plan to collect additional images of various pest species to expand the dataset, further enhancing the model’s applicability to a wider range of pest detection scenarios.
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- article
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- en
- Landing Page
- https://doi.org/10.3389/finsc.2025.1635439
- https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2025.1635439/pdf
- OA Status
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- References
- 31
- OpenAlex ID
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https://openalex.org/W4414528040Canonical identifier for this work in OpenAlex
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https://doi.org/10.3389/finsc.2025.1635439Digital Object Identifier
- Title
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GIWT-YOLO: an efficient multi-scale framework for real-time Scolytinae pests detectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-09-26Full publication date if available
- Authors
-
Jingwei Liu, Yongke Li, Lei Wang, Yunjie Zhao, Bowen Mao, Pengying WangList of authors in order
- Landing page
-
https://doi.org/10.3389/finsc.2025.1635439Publisher landing page
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https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2025.1635439/pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/insect-science/articles/10.3389/finsc.2025.1635439/pdfDirect OA link when available
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0Total citation count in OpenAlex
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