Abstract
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.
Highlights
This article presents a sea-sky-line detection method based on a Gaussian mixture model (GMM) and image texture features
To avoid the unexpected convergence direction caused by an inferior initialization condition during the process of iterative calculation, a novel primitive semantic segmentation is applied to estimate a prior probability distribution of marine images
Texture features are extracted by calculating the gray level co-occurrence matrix (GLCM) of the image to establish a superior initialization distribution for GMM
Summary
With the development of robotic structure designing and fabricating, as well as the improvement in control automation technology, various specialized robots have been applied to diverse technical fields.[1,2,3] As special surface robots, the unmanned surface vehicles (USVs) have received extensive attention and play an important role in. Liang et al.[16] proposed a sea-skyline detection method based on linear fitting They adapted a clustering algorithm to filter candidate points and located the subregion that covers the sea-sky-line through texture features, which improves the robustness of detection results. Our main contribution is segmenting the marine image by utilizing the GMM and proposing a method for estimating the prior categorical distribution of the pixel samples. The second section describes the sea-sky-line detection model and the segmentation of marine images. To segment marine images more accurately, maximum likelihood estimation is introduced to maximize the posterior probability of observed data and calculate the parameters of the GMM q^ 1⁄4 arg max logðPðX jqÞÞ ð3Þ q. A better initialization condition reduced the number of iterations in EM steps and computation time
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