Vision AI for security

Ultralytics YOLO26 for video security

Build computer vision systems for perimeter monitoring, intrusion detection, people and vehicle detection, and workplace safety. Integrate Ultralytics YOLO with existing video streams and deploy at the edge, in the cloud, or on-premises.

Analyze live video with real-time detection and tracking
Works with compatible RTSP streams and VMS workflows
Deploy across GPUs, servers, edge devices, and supported smart cameras
Low-latency inference on optimized hardware

Trusted by the world's leading organizations

How Ultralytics YOLO tackles security

Real-time AI that works with your team

Fewer irrelevant alerts, scalable stream processing, and deployment at the edge, in the cloud, or on-premises.

Fewer irrelevant alerts: Flag people, vehicles, and intrusions in live video.
Stream processing: Analyze compatible camera and VMS streams as the site scales.
Workplace safety: Read plates and check protective equipment from the same pipeline.
Private deployment: Run at the edge, in the cloud, or on-premises.

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What our customers are building

Security

Camerite cuts development time 25.2%

Analyzes live streams for people and vehicles, and turns cameras of any brand into alerts for city monitoring.

25.2%: Faster AI service implementation
7.5%: Less development time for each new service

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Videologic Analytics detecting vehicles and pedestrians in a city square

Video surveillance

Videologic Analytics scales to 10K AI camera licenses

Fine-tunes Ultralytics YOLO on existing security cameras to improve detection and reduce false alarms.

4,000+: Certified security companies served across Spain
Real-time: Threat detection built into existing security cameras

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Proven results in security

From threat detection to access control and workplace safety, recent research shows what Ultralytics YOLO delivers for security teams.

Results reported in published research.

Threat detection95.4%

Spot weapons in live video

Ultralytics YOLO26 detects weapons in surveillance footage at 95.4% mAP, and clears more than double the frames per second of earlier YOLO models on the same edge hardware.

Fierro Silva et al., Frontiers in Computer Science, 2026

Access control & LPR89%

Read plates and vehicles in one pass

An Ultralytics YOLO pipeline detects plates at 89% AP and turns each vehicle into a record of plate, color, make and model at 18 to 22 FPS.

Islas-Yañez et al., Sensors, 2026

Workplace safety7.3 ms

Check PPE compliance in real time

An Ultralytics YOLO11 safety monitor flags missing helmets, vests and glasses at 96.9% mAP in 7.3 ms a frame, down from 170 ms for earlier approaches on the same dataset.

Rahman et al., Computers, 2026

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Our models' impact

Streamline processes across industries with our cutting-edge vision AI models. Speed, accuracy and ease-of-use powered by Ultralytics.

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GitHub stars

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Downloads

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Ultralytics YOLO usages / day

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Open-source contributors

Try YOLO26 inference

Drag and drop an image to see real-time object detection

Input

Upload an image or select an example to test Crowd Detection YOLO26x. Predict Docs

Live Demo

Drop an image here

or click to browse

Supports JPEG, PNG, WebP, AVIF, HEIC, JP2, TIFF, BMP & more (max 10MB)

Or try an example

Confidence

0.25

IoU

0.70

Image Size

640

Prediction results will appear here.

Deploy your model to get a dedicated inference endpoint with API key authentication — then replace the placeholder URL and key below with your deployment values. Deploy docs

import requests

# Deployment URL and API key
url = "https://your-deployment-url.run.app/predict"
api_key = "YOUR_API_KEY"

# Optional inference parameters (conf, iou, imgsz)
args = {"conf": 0.25, "iou": 0.7, "imgsz": 640}

with open("image.jpg", "rb") as f:
    response = requests.post(
        url,
        headers={"Authorization": f"Bearer {api_key}"},
        data=args,
        files={"file": f},
    )

print(response.json())

Bring Ultralytics YOLO26 into your workflow

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