Safety Helmet Detection
Safety helmet detection is an AI recognition algorithm that automatically determines whether a person is wearing a safety helmet in images or video. It automates helmet compliance monitoring in environments requiring safety management, such as construction sites and factories, complementing the limitations of manual visual inspection.
Algorithm Overview
A pipeline combining person detection with helmet presence determination outputs the following information simultaneously:
- Person position (bounding box)
- Helmet wearing status (wearing / not wearing)
- Confidence score
Performance Metrics
| Dataset | Detection Accuracy (mAP@0.5) |
|---|---|
| HELMET | 0.93 |
Edge AI Board (RV1126B) Execution Efficiency
| Algorithm | Processing Time |
|---|---|
| helmet_detect | 66ms |
Key Features
- High-precision determination: Helmets are detected with mAP 0.93 accuracy, distinguishing between wearing and not wearing
- Simultaneous multi-person detection: Batch determination of helmet status for multiple people in a single frame
- Real-time processing: 66ms fast inference on edge AI boards
- Alarm integration: Notification, audio alarm, and recording possible upon detection of non-compliance
Use Cases
- Construction site safety management (mandatory helmet zones)
- Factory hazardous area monitoring
- Mine and tunnel construction safety management
- Warehouse and logistics center safety monitoring
- Plant maintenance operation safety management
- Safety education and training effectiveness measurement
Edge AI Board Implementation
Using the RV1126B NPU, safety helmet detection is executed at 66ms. Edge processing at the camera installation location enables immediate alarming without network latency.