YOLO

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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 Technologies Hackathon projects

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

VigiAI focused on the alert feature

VigiAI is an intelligent real-time surveillance system for detecting traffic accidents and anomalies. Running on an AMD MI300X accelerator, its hybrid architecture splits tasks between local processing—using Qwen-VL 7B, YOLO v8x, optical flow, and pose estimation—and cloud analytics via DeepSeek-V4 Flash through the Fireworks AI API. To handle chaotic real-world events, it was trained on the complete UCF-Crimes dataset (over 100 GB of footage). The system processes video through a structured 7-phase pipeline: Event Detection: Identifies anomalies instantly. Localization: Pins down spatial bounding boxes and timestamps. Scene Analysis: Evaluates weather, road, and lighting conditions. Causal Inference: Infers accident causes and secondary risks. Object Tracking: Tracks and counts vehicles or pedestrians. Speed Estimation: Measures velocity using pixel-to-meter calibration. Narrative Generation: Compiles an executive summary and detailed report. VigiAI detects 14 anomaly classes (accidents, theft, fights, fires, etc.) and transmits instant alerts via WebSockets over a public Cloudflare Tunnel. The web dashboard features live camera/webcam feeds, a GPS map, historical logs, and local vs. cloud performance metrics.

Ocular Sentinel: Autonomous C4ISR VLM

Ocular Sentinel is an AI-powered Autonomous C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance) Security System that bridges the gap between lightning-fast edge detection and deep cloud reasoning. Traditional security cameras rely on human monitoring or narrow computer vision models that lack contextual awareness—costing critical seconds during an emergency. Engineered as a predictive, dual-use C4ISR platform natively on AMD silicon, Ocular Sentinel solves this by running a lightweight YOLO tripwire locally on an edge node to capture specific objects. When triggered, it beams a buffered frame sequence to a massive Cloud Vision-Language Model (Qwen2-VL accelerated by an AMD AI Notebook/AMD MI300X). Instead of just drawing bounding boxes, the VLM acts as an expert analyst—autonomously evaluating the scene's context and generating human-readable incident reports. From detecting a fire in the road to classifying a massive seismic earthquake, Ocular Sentinel turns reactive surveillance into proactive intelligence—delivering actionable insights in milliseconds to save time, protect assets, and preserve human life.

CaptionForge AI–Multimodal Video Captioning Agent

CaptionForge AI is a production-grade multimodal video captioning system developed for the AMD Developer Hackathon Video Captioning Track. Instead of directly prompting a language model with raw video frames, the system follows an evidence-first reasoning pipeline designed to maximize factual accuracy and style consistency on unseen videos. The pipeline automatically downloads videos from the provided task list, validates input, detects scene boundaries, extracts representative keyframes, transcribes speech using Whisper, extracts on-screen text using OCR, and constructs a structured temporal representation of the video's content. This multimodal evidence is then fused into a unified semantic context that is provided to Gemma 4 through the Google Gemini API to generate a canonical factual caption. The factual caption is subsequently rewritten into four required styles—Formal, Sarcastic, Humorous-Tech, and Humorous-Non-Tech—while preserving identical facts and chronology. A verification stage checks for factual consistency, hallucinations, and style adherence before generating the final output. The application is fully containerized using Docker and automatically processes /input/tasks.json, producing /output/results.json exactly as required by the competition. The architecture emphasizes modularity, scalability, asynchronous processing, robust error handling, structured logging, and production-ready engineering practices. By combining computer vision, speech understanding, OCR, temporal reasoning, and multimodal language models, CaptionForge AI delivers accurate, context-aware captions that generalize well across diverse video categories, including people, nature, sports, urban environments, food, weather, and technology.

UnBlinder-AI:For The Visually Impaired

Unblinder AI is an AI-powered assistive navigation system designed to enhance the independence, mobility, and safety of visually impaired individuals. The project combines Computer Vision, Artificial Intelligence, Natural Language Processing, voice interaction, weather awareness, and navigation technologies into a single intelligent assistant that acts as the user's eyes. Unlike conventional mobility aids such as white canes or guide dogs, VisionGuide AI provides real-time environmental understanding and contextual guidance, allowing users to navigate both familiar and unfamiliar environments with greater confidence The system is built around a pair of smart glasses equipped with a lightweight camera that continuously captures the user's surroundings from a natural first-person perspective. The live video feed is transmitted to a web application, where advanced AI models process the information in real time. Using a state-of-the-art YOLO (You Only Look Once) object detection model, the system identifies nearby objects such as pedestrians, vehicles, bicycles, staircases, doors, traffic signs, poles, benches, obstacles, road crossings, and other important environmental elements. Instead of simply listing detected objects, VisionGuide AI understands their relative positions and generates meaningful navigation instructions that are spoken to the user through voice output Beyond obstacle avoidance, VisionGuide AI also provides complete destination-based navigation. A visually impaired user can simply speak a destination, such as and the system will calculate an appropriate route using mapping services. The system is designed with scalability in mind and can be extended with several advanced capabilities in the future

First Aid AI

First Aid AIis an intelligent emergency response assistant that provides immediate first aid guidance when every second matters. During emergencies, people often panic, struggle to explain the situation, or don't know the correct actions to take. Our solution helps users respond quickly and safely using artificial intelligence. The application accepts multiple input types, including text messages, voice recordings, and videos. It analyzes the situation using multimodal AI to identify the emergency and generate accurate first aid instructions. The guidance is available as both text and spoken audio, making the app accessible to elderly users, visually impaired individuals, and anyone who cannot read instructions during a stressful situation. To ensure ease of use under pressure, the interface is intentionally simple, with clear navigation and minimal interaction required. Users can request help in just a few steps, reducing confusion during critical moments. The platform also includes an AI agent system that automates emergency communication. After analyzing the case, the system generates a concise summary of the user's condition and shares it, along with the user's GPS location, with designated emergency contacts. When needed, the same information can be sent to hospitals and ambulance services, allowing responders to understand the situation before arrival and reduce response time. First Aid AI does not replace medical professionals; it serves as an intelligent assistant during the critical minutes before help arrives. By combining multimodal AI, voice-assisted guidance, GPS location sharing, and automated emergency alerts, the application aims to improve emergency response, increase accessibility, and potentially save lives.

NikaAI — AI Quality Copilot for MSME Manufacturing

Manufacturing defects cost the global economy $2.9 trillion annually. Enterprise vision systems cost $40,000–$200,000 — completely out of reach for the 63 million MSMEs in India alone that employ over 110 million people. Nika AI solves this by turning a smartphone into a full-stack AI quality control station. Any factory worker can capture or upload an image and instantly receive a defect analysis powered by a YOLOv8s model trained on 29,354 real industrial images across 7 merged datasets, achieving 83% mAP across 17 defect classes. What makes Nika AI genuinely different is the Hallucination Shield — Monte Carlo Dropout uncertainty quantification running 30 stochastic forward passes, classifying AI confidence as High / Moderate / Low / UNCERTAIN. Instead of giving a wrong answer confidently, Nika AI tells workers when NOT to trust it. On top of detection, Gemma 4 (via Fireworks AI) acts as a domain expert, not a chatbot — delivering structured output covering Root Cause, Severity, Repairability, Prevention Strategy, and Recommended Action for every defect found. The product is complete: JWT-secured multi-role auth (Admin / Supervisor / Worker), full inspection history with filters, an analytics dashboard with defect trends, PDF report downloads per inspection, and deployment on AMD Developer Cloud using ROCm GPU acceleration with $100 hackathon credits. Stack: React 18 + TypeScript (frontend) · FastAPI + Python 3.11 (backend) · YOLOv8s + MC Dropout · Gemma 4 via Fireworks AI · PostgreSQL + SQLAlchemy · ReportLab PDF · Docker + AMD ROCm.