Deep Learning-Powered Animal Detection for Highway Safety Enhancement

Abstract

Road accidents caused by unexpected animal crossings are a major concern, especially during nighttime when visibility is poor. To address this issue, the proposed system introduces an advanced animal detection and alert framework designed to enhance road safety through continuous monitoring. The system employs a high-resolution night-vision camera to capture real-time footage of roadways. Deep learning models such as Convolutional Neural Networks (CNN) and YOLO are used to accurately identify animals even under low-light or foggy conditions. Once an animal is detected, the system immediately triggers alert signals to warn approaching vehicles, thereby reducing the chances of collision. This intelligent approach minimizes the need for human intervention and provides a scalable solution for highways and rural roads. The integration of AI-based vision technology with real-time detection ensures efficient performance and faster response. By combining automation, deep learning, and alert mechanisms, the proposed system aims to improve nighttime driving safety and prevent animal-related road accidents.