DDM: Study Of Deer Detection And Movement Using Deep Learning Techniques

Abstract

Deer Vehicle Collisions (DVCs) is a major concern in road safety that results in loss of human life, properties and wildlife. DVCs mostly occurs during the fourth quarter of the year when deer are more active andless attentive. DVCs are increasing due to the increase in number of vehicles and the absence of intelligent highway safety and alert systems. One of the most challenging issues is to detect deer and its movement on roadways during both day and nighttime to mitigate DVCs. Thus, this paper proposed a deer detection and movement, DDM technique to automate DVCs mitigation system The DDM combines computer vision, artificial intelligent methods with deep learning techniques. DDM includes two main deep learning algorithms 1) one-stage deep learning algorithm based on Yolov5 that generates a detection model (DM) to detect deer and 2) deep learning algorithm developed by python toolkit DeepLabCut to generate movement model (MM) for detecting the movement of the deer. The proposed method can detect deer with 99. 7% precision and usingDeepLabCuttoolkit on the detected deer we can ascertain if the deer is moving or static with an inference speed of $\theta$. 29s.