Machine learning based Obstacle and wild animal detection for avoiding accidents on Railway Tracks

This study focuses on the critical task of detecting wild animals and obstacles in their natural environment. The proposed algorithm leverages deep learning techniques to classify and identify animals and obstacles based on image data, providing an efficient means of monitoring them. Given the diversity of animals and obstacles, manual identification can be challenging, making automation essential. By applying advanced image processing and deep learning algorithms, the system detects potential hazards and alerts train operators, helping to prevent accidents. The approach emphasizes long-range recognition for effective monitoring of wildlife and obstacles in railway environments.