A Comprehensive Survey of Technologies for Wildlife Detection and Accident Prevention

Wildlife collisions on roads and railways pose significant threats to animal populations and human safety, making wildlife detection and accident prevention critical research areas. This comprehensive survey reviews recent technological advancements to mitigate wildlife-vehicle and wildlife-train collisions. It covers the utilization of IoT (Internet of Things) devices such as cameras, sensors, and motion detectors, alongside AI (Artificial Intelligence) technologies like machine learning, image processing, and deep learning. These studies address a variety of factors, including environmental parameters, animal behaviour, road and rail track conditions, and traffic patterns, to predict and prevent wildlife collisions. Solutions for railways focus on preventing accidents using sensors and AI-driven models to monitor animal presence near tracks, while road-based systems emphasise wildlife crossing identification, behaviour analysis, and driver warning systems. With detection accuracies ranging from 75% to 93%, these technologies show potential for real-time alert systems and proactive wildlife conservation efforts. This survey highlights the advancements, challenges, and future directions for integrating smart technologies into transportation infrastructure to reduce wildlife-related accidents.