Counting animals from the Image using mask-rcnn and Yolo with GUI based on Django.

Apr 4, 2025 · This Python-based code that utilizes OpenCV's DNN module with MobileNetSSD to detect animals in the farmland.The code provides a GUI using Tkinter, allowing users to select a video file and start the animal detection process. When an animal is detected, an alert is triggered with a siren sound.

This repository provides python code for all experiments reported in this paper: Animal Detection in Man-made Environments [arxiv] . It contains modified versions of several open source repositories that were used for experimentation though not all of these were reported in the paper.

Apr 24, 2025 · The Pytorch-Wildlife library allows users to directly load the MegaDetector model weights for animal detection. We've fully refactored our codebase, prioritizing ease of use in model deployment and expansion.

Jun 26, 2024 · Learn how to implement animal detection in wildlife monitoring using Python machine learning and AI. This project includes MySQL database integration for efficient data management. Get access to free source code and documentation for easy …

we're introducing an automatic relating and counting the animals by using deep learning methods. This process gives an accurate result to detects and keep proper count of animals. Keywords: Animal Detection and Classification, Deep Learning Algorithms. I. INTRODUCTION Wildlife viewing in their natural environment is central to ecology.

Our devices are able to identify animals by capturing real-time images of the animals and storing the data in a database. We have used Python Idle of Raspbian OS, Raspbian OS Buster, OpenCV module, pretrained detection model and Raspberry Pi, camera as hardware.

To detect and place bounding boxes around individual animal instances within a raw image. The bounding boxes, serve as the boundaries around which the images are cropped to produce sub-images per observation. A certainty of 75% is enforced for each animal detection.

To remedy this, we are going to make a prototype animal counting and poaching detection system using image processing and machine learning. Our system uses a python-based code that consists of pre-trained Tensor Flow models.

Here we will try to show how Deep learning concepts like CNN can be implemented in a low powered and relatively inexpensive device. Keywords: Deep learning, Raspberry pi. 1. Introduction. Classifying animals plays a very distinguishing role in multiple professions and activities such as farmers, railway track monitoring, highway monitoring, etc.