Enhancing Road Safety: Detection of Animals on Highways During Night

Animals on highways at night can create serious accidents that put both human and wildlife lives in danger. Conventional monitoring systems are rendered useless due to poor visibility and inadequate lighting, emphasizing the necessity for a sophisticated detecting system. Using YOLOv5, a cutting- edge object detection algorithm, this study presents a way to identify animals on highways at night and reduce collisions. To improve image quality in low-light circumstances, the model uses pre-processing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and a robust Retinex model. This enhancement enables YOLOv5 to achieve improved detection accuracy on enhanced photos, increasing its effectiveness in spotting animals in low-light situations. YOLOv5 offers quick, highly accurate detection in contrast to conventional techniques like infrared and thermal imaging, which frequently lack the resolution for accurate species identification. The model, trained on a broad dataset, performed well, with a precision of 0.923, recall of 0.773, mean average precision (mAP50) of 0.802, and mean average precision (mAP50-95) of 0.567, demonstrating its competence in spotting animals in normal nighttime circumstances. Despite the model’s good performance in normal low-light situations, problems still arise in unfavorable conditions including rain, fog, and dense foliage, which can lower detection accuracy. This study aims to improve road safety and aid to wildlife conservation by minimizing highway collisions. Subsequent endeavors will center on enhancing the model’s resilience to withstand harsh circumstances and further diminish the likelihood of accidents.