Python
Aim:
Design and deploy a real-time, low-light-robust animal detection system using YOLOv8 for highways that reliably identifies animals at night and triggers timely driver/road-side alerts to reduce collisions.
Abstract:
Nighttime animal–vehicle collisions remain a significant safety and conservation challenge due to limited visibility and variable illumination from headlamps. This project proposes NightGuard-YOLOv8, a real‑time detection pipeline that integrates low‑light image enhancement with an enhanced YOLOv8 detector. The enhancement stage normalizes illumination and boosts local contrast without over‑amplifying noise, enabling the detector to recognize small and partially occluded animals under headlight glare and dim backgrounds. On a curated night‑vision dataset the system demonstrates strong precision, recall, and mAP while maintaining real‑time performance. YOLOv8, with its transformer-based backbone and anchor-free detection head, outperforms previous YOLO versions in robustness and generalization, making it highly suitable for ADAS and roadside camera deployments.
Proposed System:
The proposed system leverages the advanced capabilities of YOLOv8 for detecting animals on highways at night. Unlike older anchor-based models, YOLOv8 employs an anchor-free detection head that improves localization of small and distant animals, a common challenge in low-light highway environments. Its transformer-enhanced backbone extracts richer features, making it more resilient to noise and glare from vehicle headlights. The built-in augmentation strategies of YOLOv8, such as mosaic and mixup, enhance model robustness during training on limited datasets. The system also benefits from YOLOv8’s optimized loss functions, including CIoU and dynamic label assignment, which improve convergence and detection accuracy. This ensures the model can run efficiently in cars or roadside units, providing reliable alerts to prevent animal-vehicle collisions. Overall, the integration of YOLOv8’s modern architecture with optimized deployment makes it a practical and effective solution for enhancing road safety.
Advantage:
- YOLOv8 delivers better mAP and generalization than
- Anchor-free design → stronger detection of small, occluded animals.
- Modular and scalable.
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