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Yolov5n Smoke

Developed by keremberke
A lightweight smoke object detection model based on YOLOv5n, optimized for smoke recognition tasks
Downloads 97
Release Time : 1/4/2023

Model Overview

This model is a lightweight object detection model trained on the YOLOv5n architecture, specifically designed to detect smoke in images or videos. Suitable for security monitoring, fire warning, and other scenarios.

Model Features

High-precision Detection
Achieves 99.3% mAP@0.5 accuracy on the validation set
Lightweight Architecture
Designed based on YOLOv5n's lightweight structure, suitable for deployment in resource-limited environments
Easy to Use
Provides a clear Python API interface for easy integration into existing systems

Model Capabilities

Smoke Object Detection
Real-time Image Analysis
Video Stream Processing

Use Cases

Public Safety
Early Fire Warning System
Detects smoke in real-time through surveillance cameras to achieve early fire warning
Can identify fire hazards in advance, reducing losses
Industrial Monitoring
Factory Safety Monitoring
Monitors abnormal smoke conditions in factory environments
Prevents industrial accidents and ensures production safety
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