Wildlife Accident Prevention on Rail Track Using AI Technology

Traditional wildlife conservation and rail track monitoring systems face significant challenges. The research provides an innovative AI-driven approach to solving these problems. Motive comes from the fact that manual processes aren't perfect and put both environmental protection and train safety at risk. As a real-time solution, the proposed system combines computer vision and machine learning to automatically identify wildlife species and find problems with rail systems. It uses a planned arrangement of video cameras, preparation of high-resolution video data, and algorithms for object detection, support vector machines, convolutional neural networks, and recurrent neural networks. As a result, the system performed better than expected, correctly identifying wildlife 91.8% of the time and finding train anomalies more often. Within the precision, recall, and F1 Score metrics, the proposed system performs much better than the existing system. Furthermore, the system's effectiveness can be seen in the lower number of false positives and negatives, which improves train safety. According to the results, advanced AI technology can greatly help people balance their tasks with protecting the environment. In this system animals can be detected and send to the loco pilot.