Wild Animals Detection and Alert System

Animal Detection using YOLOv5

Animal Detection in Man-made Environments using Deep Learning

Animal Detection and Classification using YOLO

A U-Net-based deep learning ensemble model for wildebeest-sized animal detection from satellite imagery. A version used for the paper accepted by Nature Communications: "Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape" (https://rdcu.be/dc8bU).

Detection of Animals in camera trapped images using RetinaNet in pytorch.

🐬 Code accompanying the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"

Animals object detection such as deer, horse, and rabbit in diverse settings using YOLOv5

Closed-loop feedback optogenetic system

Image Processing and Deep Learning algorithm to detect leopards from a live camera feed.

Agri-Pal is the simplest solution to aid a farmer in Agriculture - Crop and Poultry Farming. Agri-Pal is a simple Plug n Play device ensuring Disease Detection and Animal Breach Detection.

An application which uses the camera to detect and identify animals, sounding an alert after their detection and emailing the owner about it

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.

YOLOv8 Animal Detection for Embedded Systems. 97% test accuracy in just 400kb (about the same size as the photos it classifies or 1 second of video). Various quantization, pruning, and distillation techniques for vision models are explored.

Real-time CCTV animal detection pipeline trained on Animal datasets using YOLOv8 for high‑accuracy object recognition.

Sem. VI Neural networks created to detect animals hidden in their natural environments.

HSE project. Idea to create animal detection system with voice guidance