Shoreline Detection and Land Segmentation for Autonomous Surface Vehicle Navigation with the Use of an Optical System

Abstract

Autonomous surface vehicles (ASVs) are a critical part of recent progressive marine technologies. Their development demands the capability of optical systems to understand and interpret the surrounding landscape. This capability plays an important role in the navigation of coastal areas a safe distance from land, which demands sophisticated image segmentation algorithms. For this purpose, some solutions, based on traditional image processing and neural networks, have been introduced. However, the solution of traditional image processing methods requires a set of parameters before execution, while the solution of a neural network demands a large database of labelled images. Our new solution, which avoids these drawbacks, is based on adaptive filtering and progressive segmentation. The adaptive filtering is deployed to suppress weak edges in the image, which is convenient for shoreline detection. Progressive segmentation is devoted to distinguishing the sky and land areas, using a probabilistic clustering model to improve performance. To verify the effectiveness of the proposed method, a set of images acquired from the vehicle’s operative camera were utilised. The results demonstrate that the proposed method performs with high accuracy regardless of distance from land or weather conditions.

Sensors and Sensor’s Fusion in Autonomous Vehicles

“…Autonomous surface vehicles with optical systems can be used during shoreline detection and land segmentation [ 9 ]. Hozyn and Zalewski believe that optical systems can interpret the surrounding landscape.…”

Tracking of Unmanned Aerial Vehicles Using Computer Vision Methods: A Comparative Analysis

“…Otherwise, the bag is marked negatively. The task of the method is to learn the concept from the training kit for the correct labelling of bags [7].…”

“…The significant progress of computer vision technique has been demonstrated in many research areas, such as intelligent surveillance systems, autonomous vehicles, or industrial automation [7][8][9][10]. Cheaper cameras and faster computers, as well as more sophisticated algorithms, facilitate engaging computer vision in a wide range of real-time applications [11].…”

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

“…Place et al [34] proposed a RefineNet-based solutio Nice et al [35] proposed a new sky pixel detection system that can select mean-shift se mentation, K-means clustering, and Sobel filters to detect sky pixels. Hożyń and Zalews [36] proposed a solution of adaptive filtering and progressive segmentation.…”

“…Ahmad et [41] proposed a fusion of edge-less and edge-based approach to detect the horizon. Shan et al [42] proposed a superpixel-based approach, which works for robotic navigatio McGee2005, Liu2017, Mattos2018, Song2018, Ye2019, Dev2017, Beuren2020, Tighe2013, Mihail2016, Tsai2016, Liu2016, Vargas2019, Dev2019, Fu2019, Place2019, Nice2020, and Ho ży ń2020 refer to [2,10,12,[23][24][25][26][27][28][31][32][33][34][35][36]39], respectively. Some related studies focus on the sky and ground segmentation in the planetary rover scenario.…”