Animal Detection using YOLOv5

Wild Animals Detection and Alert System

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.

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

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

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.

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.

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

Temporarily hidden. Contact alex@sandergi.com if you need access before early 2026.

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

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

Urban Disaster Monitor: AI-powered computer vision app using YOLOv11 to detect and classify civilians, rescuers, and animals in urban disaster scenarios.