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.
Highlights
Autonomous surface vehicles (ASVs) have received attention recently due to their application in many fields, including military operations, environmental protection, coastal guard patrol inspection and sea rescue [1]
The proposed method was developed for shoreline detection and land segmentation for the ASV
Since the method will be implemented in a practical solution, we were interested in source datasets, such as Singapore Maritime Dataset (SMD) and Marine Obstacle Detection Dataset testing it under conditions weofexpect a real environment
Summary
Autonomous surface vehicles (ASVs) have received attention recently due to their application in many fields, including military operations, environmental protection, coastal guard patrol inspection and sea rescue [1]. A land segmentation algorithm, dedicated to ASVs and based on traditional image processing, was proposed in [17] In this approach, segmentation is performed in two steps: firstly, a straight line separating land and sea is determined, and secondly, a line separating land and sky is distinguished. The performance, which uses images acquired by the ASV gradually improves during the self-training step Another approach utilises the multistage segmentation algorithm, as described in [25]. The methods mentioned above for ASV applications employ a set of parameters or labelled images To overcome these weaknesses, we developed a traditional approach based on progressive segmentation, which utilises only few parameters in order to be executed.
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