Animal intrusion detection system using Mask RCNN.

This project is to build an Animal Intrusion Detection System that will alert the user or the respective authorities of sightings of animals in real time if it is detected entering the village or human establishment with the distance from the border. We will achieve this by training the model with the Mask RCNN algorithm. Mask R-CNN, sometimes known as Mask RCNN, is the most advanced Convolutional Neural Network (CNN) for instance and picture segmentation. Faster R-CNN, a region-based convolutional neural network, served as the foundation for Mask R-CNN. While training the model we will feed the animal photos with labelling. This will enable the detection and recognize the animal. This project was mainly developed while keeping elephants in mind. Since elephants are poached regularly for trading, meat, tusk, and entertainment purposes. Detecting animal intrusion using image processing helps the system send out alert messages to the user and respective authorities. This real-time implementation will help avoid and reduce animal-human accidents and save human properties from damage. This will also help the residents take quick action to find solutions. [ABSTRACT FROM AUTHOR]