Summary of Ensuring Uav Safety: a Vision-only and Real-time Framework For Collision Avoidance Through Object Detection, Tracking, and Distance Estimation, by Vasileios Karampinis et al.
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation
by Vasileios Karampinis, Anastasios Arsenos, Orfeas Filippopoulos, Evangelos Petrongonas, Christos Skliros, Dimitrios Kollias, Stefanos Kollias, Athanasios Voulodimos
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a deep-learning framework for detecting, tracking, and estimating the distance of non-cooperative aerial vehicles using optical sensors. The framework utilizes monocular cameras to perceive obstacles and navigate around them. A separate lightweight encoder-decoder network is employed for efficient and robust depth estimation. Unlike previous approaches, this method formulates the problem as image-to-image translation. The object detection module identifies and localizes obstacles, conveying this information to both the tracking module and the depth estimation module. The approach is evaluated on the Amazon Airborne Object Tracking (AOT) Dataset, which is the largest known air-to-air airborne object dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine flying a drone or an airplane safely without human intervention. To make that happen, researchers need to detect and track other aircraft in mid-air. This paper shows how to use special cameras to do just that. It’s like having eyes on the back of your head! The system uses computer vision to find and follow other planes, even if they’re not cooperating. The goal is to help create fully autonomous airplanes and make air travel safer. |
Keywords
» Artificial intelligence » Deep learning » Depth estimation » Encoder decoder » Object detection » Object tracking » Tracking » Translation
