YOLO AI technology page Top Builders

Explore the top contributors showcasing the highest number of YOLO AI technology page app submissions within our community.

YOLO

YOLO (You Only Look Once) is a state-of-the-art, real-time object detection algorithm that can quickly detect and locate objects within an image or video. The YOLO architecture works by taking an input and separating it into a grid of cells and each of these cells is in charge of detecting objects within that region. YOLO returns the bounding boxes containing all the objects in the image and predicts the probability of an object being in each of the boxes and also predicts a class probability to help identify the type of object it is. YOLO is a highly effective object detection algorithm and making YOLO and open-source project led the community to make several improvements in such a limited time.

General
Relese date	2015
Author	Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
Paper	(https://arxiv.org/abs/1506.02640)
Type	Object detection algorithm

YOLO - Resources

Learn even more about YOLO!

v7 Labs Blog "YOLO: Algorithm for Object Detection Explained".
YOLOv5 Repository Object detection architectures and models pretrained on the COCO dataset.
YOLOv6 Web demo Gradio demo for YOLOv6 for object detection on videos.
Hugging Face Spaces Test YOLOv7 in the browser with Hugging Face Spaces.

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YOLO AI technology page Hackathon projects

Discover innovative solutions crafted with YOLO AI technology page, developed by our community members during our engaging hackathons.

CleanNet

CleanNet aims to develop a strategic framework for optimizing the deployment of 5G network nodes. The goal is to ensure comprehensive coverage while minimizing exposure to air pollution. By leveraging geospatial data, population density simulations, and air quality indices (AQI), the project identifies optimal node locations that balance network efficiency with environmental considerations. CleanNet aims to develop a strategic framework for optimizing the deployment of 5G network nodes. The goal is to ensure comprehensive coverage while minimizing exposure to air pollution. By leveraging geospatial data, population density simulations, and air quality indices (AQI), the project identifies optimal node locations that balance network efficiency with environmental considerations.

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.

DietDeep AI-Powered Nutrition Guide

DietDeep is an AI-powered meal planner that personalizes dietary recommendations based on user goals and meal images. It utilizes DeepSeek-R1 for meal plan generation, a pretrained YOLOv10 model for food classification, and the USDA API for nutrition data. Users enter dietary goals, upload meal images, and receive customized meal plans with portion sizes and nutritional values. The system intelligently combines detected food items with nutritional data to create well-balanced meals. Built with Streamlit for an interactive UI, DietDeep aims to simplify healthy eating using AI, making personalized nutrition accessible and efficient for individuals with diverse dietary needs.

AstroCleanAI -- AI-Powered Space Agent

AstroCleanAI is an advanced AI-powered space debris tracking and risk prediction system designed to enhance space safety. With the increasing amount of space junk threatening satellites and future missions, managing debris is more critical than ever. AstroCleanAI leverages artificial intelligence to detect, classify, and predict debris behavior, ensuring proactive collision avoidance and efficient satellite operations. 🛰️ Key Features: Real-Time Space Debris Detection & Classification – Uses deep learning models (YOLOv5, ResNet-50) to analyze satellite imagery and radar data, identifying debris based on size, trajectory, and risk level. AI-Powered Collision Risk Prediction – Implements an XGBoost-based risk assessment model to predict satellite-debris collision probabilities and suggest optimal avoidance maneuvers. Interactive Space Debris Simulation – Visualizes real-world debris movement and potential collision risks using 3D orbital mechanics modeling. Public Engagement Dashboard – Allows researchers, students, and enthusiasts to explore real-time space debris tracking and AI-driven insights. 🛠️ Technology Stack: AI & Machine Learning: TensorFlow, PyTorch, YOLOv5, ResNet-50, XGBoost Space Data & Analysis: NASA’s Open TLE Data, OpenCV for satellite imagery processing Web & Visualization: Streamlit, Matplotlib (3D trajectory simulation), Flask backend 🌍 Impact & Future Scope: AstroCleanAI contributes to sustainable space exploration by mitigating debris-related risks, preventing costly satellite damage, and optimizing space traffic management. Future enhancements may include integrating real-world satellite telemetry, collaborating with space agencies for improved predictions, and developing an autonomous debris mitigation system capable of orchestrating deorbiting maneuvers.

UrbanGrid

Urban Navigators is an innovative traffic management system leveraging AI and advanced computer vision models to revolutionize urban traffic management. The platform consists of two panels: Admin Panel: A comprehensive dashboard providing insights into active accidents, traffic analytics, signal control for specific regions, e-challan management, and emergency response coordination. AI models like YOLOv11 enable accident detection, license plate recognition, and dynamic traffic signal adjustments based on vehicle count, offering real-time operational efficiency. User Panel: Provides traffic updates through an interactive map, accident reporting capabilities, e-challan information with payment options, and a traffic updates section for better route planning. The system includes three operational modes—Normal, AI Mode, and Night Mode—to adapt to different traffic conditions. This hackathon project showcases the potential of AI to enhance urban mobility, reduce congestion, and improve road safety.

BlindNav

BlindNav is a navigation aid tailored for visually impaired individuals, combining state-of-the-art AI models for seamless interaction. By leveraging YOLOv10 for object detection and tracking, the app identifies both static and dynamic objects in real-time. Whisper captures voice input from users, while O1 generates context-aware, concise responses to user questions about their surroundings. Users can upload or capture images, and the app processes them to give auditory feedback based on object tracking results. BlindNav aims to provide an intuitive and practical navigation solution for visually impaired users, improving their independence and situational awareness.