Highway Collision Avoidance by Detection of Animal’s Images

Abstract

Traffic-related injuries and deaths are a serious problem that all industrialized nations are dealing with today. Object recognition techniques are employed in this study to develop a low cost and simple solution for automated detection and tracking on highways in order to avoid animal-vehicle collisions. In real-world units, a technique for measuring the animal distances from the camera mounted vehicle is also developed. Wild animal monitoring in their natural settings must be efficient and trustworthy in order to update manage decisions. Because of their effectiveness and accuracy in capturing wildlife data in an inconspicuous, continuous, and massive volume, automatic covert camera traps or cameras are becoming extremely popular as a tool for monitoring wildlife. Hand-taking a massive number of photos and films from camera setups is very costly and tedious. It is a significant barrier for researchers and environmental scientists who want to observe wildlife in a natural setting. This research presents a structure for developing automated animal detection in the wild, with the goal of creating an automated wildlife monitoring system, based on current breakthroughs in deep learning methods. In aspects of recognition, the suggested method attains a total precision of about 85.51 percent.