Real-Time Monocular Obstacle Detection Based on Horizon Line and Saliency Estimation for Unmanned Surface Vehicles

2021 ◽  
Author(s):  
Jingyi Liu ◽  
Hengyu Li ◽  
Jun Liu ◽  
Shaorong Xie ◽  
Jun Luo
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicles ◽  
Horizon Line

Towards a Real Time Obstacle Detection System for Unmanned Surface Vehicles

2019 ◽  
Author(s):  
Mina Sorial ◽  
Issa Mouawad ◽  
Enrico Simetti ◽  
Francesca Odone ◽  
Giuseppe Casalino
Keyword(s):  
Real Time ◽  
Detection System ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicles

scholarly journals Real-time Track Obstacle Detection from 3D LIDAR Point Cloud

2021 ◽  
Vol 1910 (1) ◽  
pp. 012002
Author(s):  
Chao He ◽  
Jiayuan Gong ◽  
Yahui Yang ◽  
Dong Bi ◽  
Jianpin Lan ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Obstacle Detection ◽  
3D Lidar

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

Real-time obstacle detection with motion features using monocular vision

2014 ◽  
Vol 31 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Baozhi Jia ◽  
Rui Liu ◽  
Ming Zhu
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Motion Features

Real-time obstacle detection for Unmanned Surface Vehicle

2011 ◽  
Author(s):  
Han Wang ◽  
Zhuo Wei ◽  
Sisong Wang ◽  
Chek Seng Ow ◽  
Kah Tong Ho ◽  
...  
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicle

scholarly journals Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

2020 ◽  
Vol 10 (2) ◽  
pp. 2164
Author(s):  
Fredy Martinez ◽  
Edwar Jacinto ◽  
Fernando Martinez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Time Estimation ◽  
Obstacle Detection ◽  
Stereoscopic Vision ◽  
Human Interaction ◽  
Human Model ◽  
Service Robots ◽  
Indoor Environments ◽  
Time Operation

This paper presents a low cost strategy for real-time estimation of the position of obstacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.

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