Automated Leopard Alert And Reporting Mechanism Using Deep Learning

Abstract

Today, rapid infrastructure development is taking place in major metropolitan cities, but unfortunately, this progress often involves the destruction of forest reserves, leaving wild animals homeless. The resulting environmental invasion forces these animals to venture into the cities, posing threats to citizens. In Mumbai, there have been numerous sightings of leopards and other wild animals near forested areas. Leopards have been known to attack street dogs, people, and vehicles, making it necessary to work on this problem. This paper suggests the utilization of deep learning models and object detection techniques to detect leopards and other potential threats. By integrating this technology with security applications, citizens can be made aware of the existence of wild animals in their vicinity. This research primarily focuses on addressing the concern of leopard sightings in Mumbai. The objective is to automate leopard detection and reporting using an object detection algorithm. In the proposed system, images of leopards are collected from an existing dataset available on Roboflow, comprising a total of 1000 samples. The proposed model's performance is evaluated using Mean Average Precision (mAP) & detection speed. The proposed method achieves an impressive mAP of 95.9% at a speed of 37 frames per second.