Abstract
In order to solve the problem of high real-time, and robustness to disturbances such as scene changes and wave reflections during visual detection in unmanned surface vehicle(USV) autonomous navigation, a real-time algorithm for visual detection of high-speed USV based on deep learning was proposed. First, a neural network MobileNet was arranged to quickly extract the full-image features. Then, a detection network based on SSD was used to fuse feature maps of each layer and achieve fast and multi-scale detection. Finally, the algorithm was implemented and validated on a hardware platform embedded GPU NVIDIA Jetson TX2. The results show that the proposed algorithm can quickly detect multiple types of specific obstacle on the water with strong robustness and multi-scale detection ability, and the detection speed of single-frame video within 50 ms.
| Translated title of the contribution | A Real-Time Algorithm for Visual Detection of High-Speed Unmanned Surface Vehicle Based on Deep Learning |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 758-764 |
| Number of pages | 7 |
| Journal | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
| Volume | 41 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2021 |