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This project builds a real-time object detection system using a Raspberry Pi and a camera. It captures live video, processes it with a TensorFlow Lite model to detect specific objects, and saves important events as video files.
This project aims to develop a Flutter application capable of real-time object detection using the YOLOv8 model. We will outline the steps for training the model and integrating it into a Flutter application.
This project utilizes YOLO (You Only Look Once), a real-time object detection system. YOLO efficiently predicts bounding boxes and class probabilities for each grid cell, renowned for its speed and accuracy.
Using yolov3 & yolov4 weights objects are being detected from live video frame along with the measurement of the object from the camera without the support of any extra hardware device.
Explore real-time object detection projects developed with OpenCV and YOLO. Empowering developers with resources and inspiration in the realm of real-time object detection. 🌐💡
Applied SSD integrated with MobileNet model for object (sign gestures) detection and recognition and the model is trained using Transfer Learning, with the aim to develop a web app for real-time ASL recognition from user input & then to generate text in English.