Abstract
Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder–decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation.
Highlights
This paper proposes a novel deep semantic segmentation model for unmanned surface vehicles operating in the complex maritime environment
The results imply that the deep neural network assisted by the superpixel method has achieved improved semantic segmentation performance with slightly increased inference time
The improved segmentation performance is attributed to the Simple Linear Iterative Clustering (SLIC) superpixel method, which is good at distinguishing edges in images
Summary
Especially drones and autonomous cars, have been widely applied in our daily lives due to the recent advances in robotics and artificial intelligence Such a trend has attracted increasing attention from the maritime industry with unmanned surface vehicles (USVs) being rapidly developed. A marine environment in which a USV operates is typically dynamic and unpredictable influenced by sudden high-speed incursions on the route, weakness in environmental awareness due to waves and sea fog, and signal disruptions, etc. Various sensors such as radar, LiDAR and inertial measurement units can be used for environment perception and localisation. Among several vision processing algorithms, the classical background subtraction method presents a high false negative rate under the harsh marine environment, which makes it unsuitable to navigate
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