Name
Abstract | ||
|---|---|---|
Real-time object detection is crucial for many applications of Unmanned Aerial Vehicles (UAVs) such as reconnaissance and surveillance, search-and-rescue, and infrastructure inspection. In the last few years, Convolutional Neural Networks (CNNs) have emerged as a powerful class of models for recognizing image content, and are widely considered in the computer vision community to be the de facto standard approach for most problems. However, object detection based on CNNs is extremely computationally demanding, typically requiring high-end Graphics Processing Units (GPUs) that require too much power and weight, especially for a lightweight and low-cost drone. In this paper, we propose moving the computation to an off-board computing cloud, while keeping low-level object detection and short-term navigation onboard. We apply Faster Regions with CNNs (R-CNNs), a state-of-the-art algorithm, to detect not one or two but hundreds of object types in near real-time. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IRC.2017.77 | 2017 First IEEE International Conference on Robotic Computing (IRC) |
Keywords | DocType | ISBN |
Robot Vision,Object Detection,Unmanned Aerial Systems,Convolutional Neural Networks | Conference | 978-1-5090-6725-1 |
Citations | PageRank | References |
2 | 0.51 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jangwon Lee | 1 | 2 | 0.51 |
| Jingya Wang | 2 | 9 | 1.05 |
| D. Crandall | 3 | 2111 | 168.58 |
| Selma Sabanovic | 4 | 302 | 44.66 |
| Geoffrey Fox | 5 | 4070 | 575.38 |