Human Wildlife Conflict Mitigation Using YOLO Algorithm

Human wildlife conflict occurs when encounters between humans and wildlife result in negative outcomes such as loss of property, livelihood, and even life for both humans and wildlife, which has a significant impact on the eco system. Interactions between people and wild animals are becoming more common as the human population grows and biodiversity increases. Detecting wild animals and preventing them from entering human habitats, resulting in human-wildlife conflict. It is intended to use machine learning to identify various types of endangered animals. The Dataset contains over 10,000 images in which 7,000 images are training images and 3,000 images are testing images. Our proposed solution employs You only Look once (YOLO) algorithm which is a Convolution Neural Network (CNN) to detect and identify the wild animals using Machine Learning model. The proposed method yields 94% accuracy in detecting the endangered wild animals through the computer vision algorithms. The achieved accuracy percentage is comparatively the highest among the existing model [2] because of the YOLO V4 model.