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RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance.

Jongwon Kim1, Jeongho Cho1

  • 1Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Korea.

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Summary

A new real-time onboard object detection system, RGDiNet, integrates RGB aerial images and depth maps for improved detection of small, sparse objects in challenging aerial environments. This system enhances autonomous flight and surveillance capabilities for unmanned aerial vehicles (UAVs).

Keywords:
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Area of Science:

  • Computer Vision and Artificial Intelligence
  • Robotics and Autonomous Systems
  • Remote Sensing and Photogrammetry

Background:

  • Object detection is crucial for Unmanned Aerial Vehicle (UAV) autonomous flight and air-to-ground surveillance tasks like search and rescue and disaster analysis.
  • Current multimodal object detection advancements, while successful in autonomous driving, face limitations for onboard UAV processing due to small object sizes, sparse distribution, environmental variations, and UAV payload constraints.
  • Existing vision-based methods struggle with the unique challenges of aerial imagery, necessitating novel approaches for real-time onboard object detection.

Purpose of the Study:

  • To propose a novel, real-time onboard object detection architecture specifically designed for UAV aerial data processing.
  • To address the limitations of conventional methods in detecting small, sparsely distributed, and occluded objects in complex aerial environments.
  • To enhance the efficiency and accuracy of object detection for UAV applications by integrating RGB imagery with depth map data.

Main Methods:

  • Development of the RGB aerial image and point cloud data (PCD) depth map image network (RGDiNet), a real-time onboard object detection architecture.
  • Utilizing a faster region-based convolutional neural network as the baseline detection network.
  • Inputting an RGD (integration of RGB aerial image and Light Detection and Ranging (LiDAR) PCD-reconstructed depth map) for computational efficiency.

Main Results:

  • Performance tests and evaluations were conducted using hand-labeled aerial datasets under diverse operating conditions.
  • The proposed RGDiNet demonstrated superior performance in detecting vehicles and pedestrians compared to conventional vision-based methods.
  • The architecture is optimized for computational efficiency, making it suitable for limited UAV payloads.

Conclusions:

  • The RGDiNet architecture offers a significant advancement in real-time onboard object detection for UAVs.
  • Integration of RGB imagery and depth maps effectively addresses challenges posed by aerial surveillance scenarios.
  • The proposed method enhances the feasibility of complex autonomous tasks for UAVs, including surveillance and search and rescue.