A pre-trained YOLO based hand detection network.

Complete Hands-On YOLO Object Detection Tutorial

Poker Hand Detection and Analysis using YOLO11.

Hand detection using YOLOv7 with COCO dataset.

The application is developed using Python3, OpenCV and with concepts of Neural Networks it is trained on DarkNet 53 and for the real time object detection I am using Yolo v3. It detects the 1 hand representation of number 1-10 which will be pointed by the user in real time using American Sign Language

Mahjong detection using YOLO v11 and locating the winning hand using RANSAC

Gesture recognition helps computers to understand human body language. This helps to build a more potent link between humans and machines, rather than just the basic text user interfaces or graphical user interfaces (GUIs). In this project for gesture recognition, the human body's motions are read by computer camera. The computer then makes use of this data as input to handle applications. The objective of this project is to develop an interface which will capture human hand gesture dynamically and will control the volume level. For this, Deep Learning techniques such as Yolo model, Inception Net model+LSTM, 3-D CNN+LSTM and Time Distributed CNN+LSTM have been studied to compare the results of hand detection. The results of Yolo model outperform the other three models. The models were trained using Kaggle and 20% of the videos available in 20 billion jester dataset. After the hand detection in captured frames, the next step is to control the system volume depending on direction of hand movement. The hand movement direction is determined by generating and locating the bounding box on the detected hand.

This repository is used to store the code that uses the combination of mediapipe and yolov5, which realizes the detection of a specific area with yolo around the hand detected by mediapipe

Hand Detector

🤚 Hand detection using YOLO

This project made to detect each letter in one-word Latin handwriting using the You Only Look Once (YOLO) network, one of the Convolutional Neural Networks’ algorithms. The experimental result shows processing time per one word is 0.0776 seconds with an accuracy of 81.93%.

This project uses Python, OpenCV, and YOLOv8 for Hand Gesture Detection, enabling real-time data recording, annotation, and model development to recognize specific hand gestures.

Multi-object tracker that provides functionality to track objects detected by the YOLO object detection system, combined with information provided by the MediaPipe hand tracker.

Hand detection using darknet and keras

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This is the darknet base segmentation platform with deep neural network and It also have the ability to perform object detection and segmentation at the same time. In the iNES lab of Gachon University, we worked on this idea and we successfully got efficient results. In the same hand, our new network is computationally better and faster than Segnet,FCN, and Yolo family. Therefore, we would like to share all the essential knowledge regarding to it.

This project uses YOLOv8 as the framework and the hand dataset for model training. Having detailed Readme documentation and project structure documentation, it is highly user-friendly for beginners who are new to YOLO.

This is a GUI that uses machine learning techniques such as CNN's and YOLO object detection to tell a user if they have correctly signed a letter in American sign language.

A real-time finger counting system using YOLO-based object detection. The project detects hand gestures and counts fingers from live video or images.

An AI-powered cheating detection system using MediaPipe, YOLO, and a multi-stage alarm engine. Tracks eye gaze, head pose, hand movements, and phone usage in real time with a clean browser UI.

This is a practice project done during AI Vicharana Shala Bootcamp at IIT Ropar

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It was developed with the Yolo model and the mediapipe library was used for hand detection.

This project uses YOLO V8 for detecting hand bone fractures in X-ray images. Trained on a dataset of 1200 images across six fracture categories, YOLO V8 improves accuracy and speed over YOLO NAS, enhancing the detection of subtle fractures for timely and reliable diagnoses in clinical settings.

This project implements a real-time sign language recognition system using computer vision and artificial intelligence. The model is based on YOLO (You Only Look Once), an object detection architecture optimized for fast and accurate recognition of hand gestures representing sign language letters.

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Contribute to the open source community,A hand detection model base on yolo11n

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