Efficient obstacle detection based on prior estimation network and spatially constrained mixture model for unmanned surface vehicles

2020 ◽  
Author(s):  
Jingyi Liu ◽  
Hengyu Li ◽  
Jun Luo ◽  
Shaorong Xie ◽  
Yu Sun
Keyword(s):  
Mixture Model ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicles ◽  
Constrained Mixture

scholarly journals A sea-sky-line detection method based on Gaussian mixture models and image texture features

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989211
Author(s):  
Wanhui Yang ◽  
Hengyu Li ◽  
Jingyi Liu ◽  
Shaorong Xie ◽  
Jun Luo
Keyword(s):  
Gaussian Mixture ◽  
Obstacle Detection ◽  
Distribution Model ◽  
Line Detection ◽  
Detection Accuracy ◽  
Gray Level ◽  
Data Set ◽  
Unmanned Surface Vehicles ◽  
Public Data ◽  
Occurrence Matrix

This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.

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

Low cost optronic obstacle detection sensor for unmanned surface vehicles

2015 ◽  
Author(s):  
Andrea Sorbara ◽  
Enrica Zereik ◽  
Marco Bibuli ◽  
Gabriele Bruzzone ◽  
Massimo Caccia
Keyword(s):  
Low Cost ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicles
Sign in / Sign up

Export Citation Format

Share Document