Drone-Based Small Object Detection for Traffic Monitoring and Emergency Response

This project develops a drone application aimed at detecting small objects to enhance traffic monitoring, fire detection, and parking management. Utilizing a customized dataset, the application leverages Keras RetinaNet, specifically configured with optimized anchor sizes for small object detection. Designed for edge devices, the system supports RTSP streaming over TCP, enabling real-time processing of video frames at the required frames per second (FPS). Furthermore, the model can be trained on specialized datasets, such as those for detecting fire or smoke, providing a proactive solution for countering wildfires and ensuring effective emergency response.

Year
2019-2020

Introduction

In an era where real-time data analysis is crucial for public safety and operational efficiency, this project presents a state-of-the-art drone application designed for the detection of small objects. Targeting critical use cases such as traffic monitoring, fire detection, and parking management, the application leverages advanced computer vision techniques to enhance situational awareness. Utilizing Keras RetinaNet, the model is specifically trained on a customized dataset with optimized anchor sizes for small object detection, ensuring high accuracy and reliability.

Designed for deployment on edge devices, the application facilitates RTSP streaming from drones over TCP, allowing for real-time processing of video frames at the necessary frames per second (FPS). This capability not only enables immediate insights but also allows for proactive responses in emergency situations, such as detecting wildfires through specialized training on fire or smoke datasets. By integrating this innovative technology, the project aims to significantly improve monitoring capabilities and contribute to enhanced public safety and operational effectiveness.

Architecture Components

  1. Drone Hardware

    • Camera Module: Captures video footage in real time, capable of streaming high-quality video over RTSP.

    • Processing Unit (Edge Device): A powerful edge computing unit (e.g., NVIDIA Jetson, Raspberry Pi with AI acceleration) processes the video stream and runs the object detection model.

  2. RTSP Stream Setup

    • RTSP Server: The camera module streams video to the RTSP server on the drone using the RTSP protocol. This enables remote clients to access the video feed over TCP.

  3. Object Detection Pipeline

    • Video Stream Processor: This component receives the RTSP video stream and extracts frames for analysis.

    • Keras RetinaNet Model: A containerized model deployed on the edge device performs real-time object detection on the extracted frames. The model is trained on a customized dataset optimized for detecting small objects.

  4. Data Handling

    • Frame Processing: Frames are processed sequentially, and detections are logged, including bounding box coordinates and object classifications.

    • Alert Management: If small objects of interest (e.g., vehicles, fires) are detected, alerts are generated based on predefined criteria (e.g., size, location).

  5. Communication and Control

    • TCP Communication: The edge device communicates with a central server or dashboard over TCP for remote monitoring and control. This includes sending alerts, video feed summaries, and other relevant data.

    • WebSocket/REST API: Optionally, a WebSocket or REST API can be implemented for real-time updates and command control, allowing users to interact with the drone and receive status updates.

  6. Cloud Integration (Optional)

    • Cloud Storage: Detected object data and processed video clips can be stored in cloud storage (e.g., AWS S3) for further analysis, historical records, or machine learning retraining.

    • Analytics Dashboard: A web-based dashboard that aggregates data from multiple drones for centralized monitoring and analytics. This can include visualizations of detected objects over time and geographic locations.

Drone Applications

Data Flow

  1. Video Capture and Streaming:

    • The camera captures video and streams it via RTSP to the edge device.

  2. Frame Extraction and Processing:

    • The video stream is continuously processed, extracting frames at the specified FPS (e.g., 30 fps). Each frame is sent to the Keras RetinaNet model for small object detection.

  3. Object Detection:

    • The model analyzes each frame for small objects, generating detection results (e.g., bounding boxes and confidence scores).

  4. Alert Generation and Communication:

    • If specified objects are detected, alerts are generated and sent to a central server over TCP. The edge device can also communicate video summaries or processed frames if needed.

  5. Optional Cloud Integration:

    • Processed data can be sent to the cloud for storage and further analysis, while analytics dashboards provide insights and visualizations.

Design Considerations

  • Real-Time Processing: The architecture is optimized for low-latency processing to enable timely detection and response.

  • Resource Management: Utilizing edge devices minimizes the need for constant high-bandwidth connections to cloud resources, optimizing operational efficiency and reducing costs.

  • Scalability: The system can scale by adding more drones with similar architectures, each capable of independent streaming and processing.

  • Reliability: TCP provides reliable communication between the edge device and the central server, ensuring that alerts and data are accurately transmitted.