Enhancing Public Safety with Emergency Vehicle Detection on Matrice

Emergency Vehicle Detection

Timely recognition of emergency vehicles is vital in urban environments. With AI-powered computer vision, it’s now possible to detect ambulances, fire trucks, and police vehicles in real time to improve road safety, automate traffic systems, and reduce emergency response delays.

Our application focuses on detecting emergency vehicles using a high-quality dataset and real-world video feeds. By training a detection model on diverse traffic scenes, we can enable accurate recognition under various lighting, weather, and motion conditions.

This blog outlines our approach to solving this use case:

  1. Dataset Preparation

  2. Dataset Annotation

  3. Model Training

  4. Model Evaluation

  5. Model Inference

  6. Model Deployment

Dataset Preparation

The dataset used in this project comprises road footage and images featuring emergency vehicles in different contexts—urban, highway, day, and night.

  • Categories: Ambulance, Fire Truck, Police Car

  • Data Items: 3436

  • Training Set: 2786

  • Validation Set: 568

  • Testing Set: 82

The data is split in to ensure generalization across unseen environments.

Dataset Summary

Dataset Samples

Model Training

We trained a YOLO-based object detection model to accurately identify emergency vehicles. The model is built to handle real-world challenges like motion blur, occlusion, and varying vehicle scales.

Training parameters:

  • Batch Size: 16

  • Epochs: 90

  • Learning Rate (lr): 0.001

  • Optimizer: auto

  • Momentum: 0.95

  • Weight Decay: 0.0005

Performance Metrics

We evaluated the trained model using mAP@50, precision, and recall on both validation and test sets.

Metric

Value (Test)

Value (Val)

mAP@50

0.90

0.87

Precision

0.90

0.85

Training Loss

Detection Preview

Model Inference

After training, the model is ready for deployment across different platforms. Export options include:

  • PyTorch (.pt)

  • ONNX

  • TensorRT

  • OpenVINO

This flexibility enables seamless real-time inference on edge devices like traffic cameras and embedded processors.

Model Deployment

Deploying the emergency vehicle detection model on Matrice is fast and efficient. The platform supports:

  • Real-time video inference

  • REST API integration

  • Visual monitoring dashboards

Use Cases:

  • Smart traffic signal prioritization

  • Public safety monitoring systems

  • Automated emergency response alerts

Conclusion

By automating emergency vehicle detection, cities can boost safety, improve emergency response times, and optimize traffic control. With Matrice, deploying robust AI models becomes simple and scalable.

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