Start Detection: Click the "Start Detection" button to begin analyzing the video. Live Alerts: The app will highlight detected animals, play a siren sound, and send an SMS alert if configured.
It allows users to directly load a variety of models including MegaDetector, DeepFaune, and HerdNet from our ever expanding model zoo for both animaldetection and classification.
Download the raw observation images from iNaturalist observations. Arrange each sub-image into a taxonomic directory structure. The below headings provide information on how to execute each step, what the process entails, and what the expected output should be.
🦁 Tracking the Wild: Real-Time AnimalIdentification with AI 🌍🚀 In this project, we’ll build a real-time animaldetection and identification system using Python, EfficientNetB0,...
This Python project, utilizing computer vision libraries like OpenCV and YOLO (You Only Look Once), provides powerful tools to automate animaldetection in wildlife monitoring.
Our devices are able to identifyanimals 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.
By leveraging computer vision techniques, this project aims to contribute to wildlife conservation efforts by enabling the detection and monitoring of various animal species.
This project aims to implement animalrecognition using YOLO, OpenCV, and the COCO object library on the Raspberry Pi 5, utilizing its enhanced processing power and GPU acceleration for efficient AI inference at the edge.
Here is the step by step implementation of object detection using OpenCV. For this you can download the Haar Cascade XML file for object detection and the sample image from here.
Here is the step by step implementation of object detection using OpenCV. For this you can download the Haar Cascade XML file for object detection and the sample image from here.