Animal Detection in Man-made Environments using Deep Learning
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Updated
Jul 6, 2023 - Python
Animal Detection in Man-made Environments using Deep Learning
Animal Detection using YOLOv5
Detection of Animals in camera trapped images using RetinaNet in pytorch.
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).
🐬 Code accompanying the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"
Closed-loop feedback optogenetic system
Animal Detection and Classification using YOLO
Image Processing and Deep Learning algorithm to detect leopards from a live camera feed.
An application which uses the camera to detect and identify animals, sounding an alert after their detection and emailing the owner about it
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.
Sem. VI Neural networks created to detect animals hidden in their natural environments.
HSE project. Idea to create animal detection system with voice guidance
Animals object detection such as deer, horse, and rabbit in diverse settings using YOLOv5
Animal detection application
A model to automatically identify and classify animals in images. It's implemented using transfer learning from pre-trained weights of EfficientNet B7. It has an average accuracy of 86%.
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