journal article·2025Open access

Improving Hornet Detection with the YOLOv7-Tiny Model: A Case Study on Asian Hornets

Yung-Hsiang Hung, Chuen-Kai Fan, Wen-Pai Wang

Computers, Materials & Continua· Volume 83· Issue 2

Abstract

Bees play a crucial role in the global food chain, pollinating over 75% of food and producing valuable products such as bee pollen, propolis, and royal jelly. However, the Asian hornet poses a serious threat to bee population... | Find, read and cite all the research you need on Tech Science Press
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Research context

OpenAlex topics: Infrared Target Detection Methodologies · Aerospace Engineering and Energy Systems · Advanced Measurement and Detection Methods

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References (22)

Show source-provided references
1. Importance of pollinators in changing landscapes for world crops10.1098/rspb.2006.3721 ↗
2. Insect pollinated crops, insect pollinators and US agriculture: trend analysis of aggregate data for the period 1992–200910.1371/journal.pone.0037235 ↗
3. Mattila · PLoS One · 2020 · 15 · e024266810.1371/journal.pone.0242668 ↗
4. Machine learning: trends, perspectives, and prospects10.1126/science.aaa8415 ↗
5. Deep learning10.1038/nature14539 ↗
6. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions10.1186/s40537-021-00444-8 ↗
7. Terven J, Cordova D. A comprehensive review of YOLO: from YOLOv1 to YOLOv8 and beyond. arXiv:2304.00501. 2023. doi:10.48550/arXiv.2304.00501.
8. You only look once: unified, real-time object detection
9. YOLO9000: better, faster, stronger
10. Redmon J, Farhadi A. YOLOv3: an incremental improvement. arXiv.1804.02767. 2018. doi: 10.48550/arXiv.1804.02767.
11. Bochkovskiy A, Wang CY, Liao HYM. YOLOv4: optimal speed and accuracy of object detection. arXiv.2004.10934. 2020. doi:10.48550/arXiv.2004.10934.
12. YOLOv7 performance comparison: a survey
13. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
14. An analysis of convolutional neural networks for image classification10.1016/j.procs.2018.05.198 ↗
15. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects
16. Squeeze-and-excitation networks
17. Squeeze-and-excitation networks10.1109/TPAMI.2019.2913372 ↗
18. SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling-based residual U-Net for automatic liver segmentation in computed tomography10.1016/j.cmpb.2021.106268 ↗
19. An improved algorithm for small object detection based on YOLO v4 and multi-scale contextual information10.1016/j.compeleceng.2022.108490 ↗
20. Project-vrljw, hornet_only dataset, roboflow universe, roboflow [Internet]. 2023 [cited 2025 Mar 12]. Available from: https://universe.roboflow.com/project-vrljw/hornet_only.
21. Bees and hornets, bees dataset, roboflow universe [Internet]. 2023 [cited 2025 Mar 12]. Available from: https://universe.roboflow.com/bees-and-hornets-20yku/bees-twjei.
22. Si gn, insect_detection dataset, roboflow universe [Internet]. 2023 [cited 2025 Mar 12]. Available from: https://universe.roboflow.com/si-gn/insect_detection-qsh9u.