Abstract
Obstacle avoidance based on a monocular camera is a challenging task due to the lack of 3D information for Unmanned Aerial Vehicle. Recent methods based on Convolutional Neural Networks for monocular depth estimation and obstacle detection become widely used. However, collision avoidance with depth estimation usually suffers from long computational time and low avoidance success rate. A new collision avoidance system is proposed which uses monocular camera and intelligent algorithm to avoid obstacles on real time processing. Several experiments have been conducted on crowded environments with several object types. The results show outstanding performance in terms of obstacles avoidance and system response time compared to contemporary approaches. This makes the proposed approach of high potential to be integrated in crowded environments.
| Original language | English |
|---|---|
| Pages (from-to) | 1-9 |
| Number of pages | 9 |
| Journal | IET Smart Cities |
| Volume | 6 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2024 |
Bibliographical note
Publisher Copyright:© 2023 Yokogawa Saudi Arabia and The Authors. IET Smart Cities published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Keywords
- IoT and mobile communications
- data structures, artificial intelligence, data analytics and machine learning
- intelligent control
- smart cities applications
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Urban Studies
- Computer Science Applications
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Artificial Intelligence
