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.

SixthSense: Haptic Vision for the Blind

SixthSense is a wearable that helps blind and low-vision people sense obstacles around them and find a clear path. A phone is mounted on the chest and watches the way ahead. On-device models turn what the camera sees into a simple readout: how near obstacles are in the left, center, and right zones, what objects are present, and whether the path is clear. That readout drives a vibration belt worn at the waist, which buzzes on the side of the nearest obstacle so the user can feel which way to move. The point is that knowing something is close is not enough. A basic vibrating cane buzzes whenever anything is near, so in a crowd it buzzes constantly without telling you where the gap is. SixthSense reads each zone separately and steers the user toward open space, so it stays useful in busy areas. The user can also ask what is ahead and hear a short spoken answer, or point the camera at a sign and have its text read aloud. The vision runs on the phone. YOLOv11n detects objects and tags each to a left, center, or right zone. Depth-Anything-V2 estimates how near things are, which sets how hard the belt buzzes. Qwen2.5-0.5B answers spoken questions about the scene, and ML Kit reads text on demand. The models run through ExecuTorch as compiled files on the phone, offline, on a Qualcomm Snapdragon 8 Elite, with the option to run on the Hexagon NPU. The phone sends a small directional packet over Bluetooth to an ESP32, which drives the belt motors. Cost is the main reason it exists. Smart canes and glasses run from about $850 to over $2,000, and only one in ten people who need assistive technology can get it, dropping to about five percent in lower-income countries. SixthSense uses a phone the user already has and a sub-$20 belt, with room to reach about $50 at scale, putting this within reach of people who are priced out today.

Data Center Ops

Data Center Ops is a real-time, on-device AI assistant that helps data center technicians inspect and assemble server racks correctly the first time. Over 1000 racks are built every week — dense meshes of cables, trays, and ports assembled at volume by labor crews cross-referencing paper guides. Roughly 80% are installed incorrectly on the first attempt. DC-Ops turns any Snapdragon phone into a smart inspection tool: point the camera at a rack and it instantly identifies 16 classes of components — compute trays, network ports, LEDs, cables, fans, drive bays, power shelves, DPUs and more — drawing live overlays on the camera feed. Everything runs entirely on-device using PyTorch and ExecuTorch, compiled to the Qualcomm QNN Hexagon Tensor Processor (HTP) NPU on the Samsung Galaxy S25 Ultra (Snapdragon 8 Elite, SM8750). This matters because data centers that manage private data have limited cloud availability but technicians need instant feedback. By running inference directly on the NPU with INT8 quantization, DC-Ops delivers low-latency, power-efficient detection with zero data ever leaving the device. We trained on 2,036 human-labeled images across four datasets, bootstrapped with a BrightData web-scraping + auto-labeling pipeline (Grounding DINO + SAM). Models and dataset are published openly on Hugging Face.

OBSERVA

OBSERVA is a privacy-first AI mobility assistant designed for blind and low-vision users who need safe, reliable navigation support in everyday environments. Unlike many tools that depend on cloud processing, OBSERVA is built to run core safety features fully on-device, including in airplane mode, so users can rely on it in low-connectivity settings and for sensitive private contexts. Our current Android prototype uses a continuous CameraX loop with low-latency audio and haptic alerts, a hazard engine with cooldown and scene memory to avoid repetitive alert spam, and an accessibility-focused interface optimized for non-visual interaction. The product direction includes robust object recognition, hazard detection, contextual navigation assistance, and multilingual interaction/translation pathways while maintaining strict local processing guarantees. We position OBSERVA in a gap between pure OCR/vision apps and infrastructure-dependent systems (e.g., code-tag ecosystems), focusing on independent mobility, comfort, and trust. Our core value proposition is simple: private, offline, safety-oriented assistive intelligence that is usable in the real world, not just in ideal network conditions.

Lumina — Embodied Spatial AI

Lumina is a real-time assistive navigation system designed for visually impaired users. It uses a camera feed (local webcam or IP camera) to continuously perceive the environment, build a persistent spatial memory, and respond to natural-language queries like "Where is my phone?" or "Find my bottle" with spoken, clock-direction navigation instructions. The system is built around a true Multi-Agent System (MAS) architecture — six autonomous agents communicate exclusively through a central Pub/Sub event bus with no direct inter-agent coupling. This enables genuine agent autonomy, fault isolation, and emergent negotiation behaviour. Core capabilities: - Real-time object detection and multi-object tracking (YOLOv8 + IoU tracker) - Monocular depth estimation with RANSAC multi-anchor metric calibration (MiDaS) - 3D spatial back-projection (X, Y, Z camera-coordinate vectors) - Persistent spatial memory with probabilistic confidence decay (Qdrant vector DB) - Illumination-invariant visual Re-ID for cross-frame object deduplication - Bird's-Eye View occupancy grid for safe lateral obstacle avoidance - ORB-SLAM visual odometry compass (drift-free heading without IMU) - LLM-driven query parsing and natural-language response generation - LLM cascade: Groq → OpenAI → Local edge SLM (llama.cpp / Ollama) → deterministic fallback - Real-time WebSocket streaming of annotated frames, agent logs, and navigation responses - Cross-session persistent user memory

Weld AI - Autonomous Multi-Agent Radiography

📌 Overview In regulated industries like oil & gas, piping, power, and manufacturing, weld structural failures can lead to catastrophic accidents. Quality assurance relies on Non-Destructive Testing (NDT) radiography, a manual, slow, and error-prone process. The Weld NDT AI Inspector transforms this workflow into a highly reliable, autonomous, and auditable multi-agent pipeline. The system is architected using Hexagonal (Ports & Adapters) design principles to keep core business rules independent of ML frameworks and databases, running securely on Google Cloud Platform (GCP) and MongoDB Atlas. 👥 The Multi-Agent Collaboration Room Inside a distributed Band.ai room, four remote agents work in sequence to ensure safe and compliant decision-making: Weld Orchestrator Agent: Directs the room flow, contrast-enhances the raw scan with CLAHE, dispatches payloads, and logs tamper-evident audit events. Weld Vision Agent: Deploys a fine-tuned computer vision model to locate and classify physical defects (e.g. porosity, slag, cracks) on grey-scale film. Weld Compliance Agent: Leverages a rules engine and Gemini to cross-reference dimensions against regulatory standard tolerances (ASME B31.3, API 1104, AWS D1.1). Weld Review Agent (Track 3 Core): A strict safety auditor that enforces mandatory overrides (e.g., any crack = mandatory reject, regardless of size) and routes ambiguous cases to an ESCALATE status for human Level III inspection. 💾 Enterprise-Grade Resiliency Dual-Database Adapter: Saves inspection records to MongoDB Atlas with automatic fallback to local SQLite storage to prevent downtime in offline industrial fields. Inference Caching: Indexes scans via cryptographic SHA-256 hashes, returning cached defect coordinates on duplicates to prevent redundant GPU overhead. Vertex AI Integration: Authenticates securely using GCP service account IAM policies, bypassing the need for static API keys.

WheatGuard

WheatGuard is an innovative, end-to-end agricultural defense platform designed to protect wheat crops from destructive fungal diseases, focusing primarily on Leaf Rust early detection. The solution bridges advanced computer vision and practical farming requirements through three core components: 1. AI & Computer Vision: Utilizing state-of-the-art object detection models to analyze drone-captured aerial imagery of fields, ensuring high-accuracy diagnosis of initial rust pustules. 2. Multi-Agent System (Band Backend): Powered by an intelligent backend orchestration layer where autonomous agents communicate seamlessly. When field sensors or edge devices lack real-time network responses, the backup agent intelligently generates localized data and fallback daily status logs. 3. Android Mobile Application: A clean, accessible native application tailored for farmers. It displays live field telemetry, processes notifications from drone sweeps, and provides clear actionable ecological recommendations to contain outbreaks before they spread across the plot. By deploying WheatGuard, farmers can minimize chemical chemical overuse, target fungicide applications effectively, and safeguard food security through proactive, modern AI technology