Animal Intrusion Detection in Farming Area using YOLOv5 Approach

Animal intrusion in the farming area causes significant losses in agriculture. It threatens not only the safety of farmers but also contributes to crop damage. Providing effective solutions for human-animals conflict is now one of the most significant challenges all over the world. Therefore, early detection of animal intrusion via automated methods is essential. Recent deep learning-based methods have become popular in solving these problems by generating high detection ability. In this study, the YOLOv5 method is proposed to detect four categories of animals commonly involved in farming intrusion areas. YOLOv5 can generate high accuracy in detection using cross stage partial network (CSP) as a backbone. This network is employed to extract the beneficial characteristics from an input image. The results of the implementation of this method show that it can detect animal intrusion very effectively and improve the accuracy of detection by nearly 94% mAP. The results demonstrate that the proposed models meet and reach state-of-the-art results for these problems.