YOLO-FES: an Improved elephant intrusion detector based on YOLOv8n
Human–elephant conflict (HEC) poses a significant threat to both biodiversity and rural livelihoods, necessitating innovative monitoring solutions that are both accurate and deployable in resource-constrained environments. Existing deep learning models often trade off detection accuracy for computat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/47336/1/YOLO-FES%20An%20Improved%20Elephant%20Intrusion%20Detector.pdf https://doi.org/10.1109/ACCESS.2025.3633664 https://umpir.ump.edu.my/id/eprint/47336/ |
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| Summary: | Human–elephant conflict (HEC) poses a significant threat to both biodiversity and rural livelihoods, necessitating innovative monitoring solutions that are both accurate and deployable in resource-constrained environments. Existing deep learning models often trade off detection accuracy for computational efficiency, limiting their real-time use on edge devices. To address this challenge, we propose YOLO-FES, a lightweight object detection model tailored for efficient and accurate elephant intrusion detection. YOLO-FES integrates three key components: (i) a FasterNet block (F) that replaces bottleneck blocks in the backbone, (ii) an Efficient Multi-scale Attention (E) module to enhance feature representation, and (iii) a Slim-neck (S) powered by GSConv for improved feature fusion. The model was trained on a diverse dataset comprising trap cameras, CCTV, drones, mobile phones, television footage, and supplemented with elephant images from COCO2017. Experimental results demonstrate that YOLO-FES reduces parameters, FLOPs, and model size by 19%, 23.5%, and 16.1%, respectively, compared to YOLOv8n, while achieving higher accuracy with +1.5% mAP@0.5 and +1.2% mAP@0.5–0.95. Edge deployment evaluations confirm real-time performance, with inference times ranging from 24.7 ms on Jetson Orin Nano to 254.2 ms on Raspberry Pi 4B. These results establish YOLO-FES as a robust, low-cost, and deployable solution for real-time elephant intrusion detection, contributing to sustainable mitigation of human–elephant conflict. |
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