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WODIS: Water Obstacle Detection Network based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

Chen, X; Liu, Y; Achuthan, K; (2021) WODIS: Water Obstacle Detection Network based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments. IEEE Transactions on Instrumentation and Measurement 10.1109/TIM.2021.3092070. Green open access

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Abstract

A reliable obstacle detection system is crucial for Autonomous Surface Vehicles (ASVs) to realise fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks such as poor detection for small objects, low estimation accuracy caused by water surface reflection and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoderdecoder structured deep semantic segmentation network, which is Water Obstacle Detection network based on Image Segmentation (WODIS), to solve above mentioned problems. The first design feature of WODIS utilises the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea-sky-line areas, an Attention Refine Module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a Feature Fusion Module (FFM) is introduced to help concatenate the multi-dimensional high-level features in the decoder network. The WODIS is tested and cross validated using four different types of maritime datasets with the results demonstrating that mIoU of WODIS can achieve superior segmentation effects for sea level obstacles to values as high as 91.3.

Type: Article
Title: WODIS: Water Obstacle Detection Network based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIM.2021.3092070
Publisher version: https://doi.org/10.1109/TIM.2021.3092070
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Obstacle detection, image segmentation, deep neural networks, autonomous surface vehicles
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10130228
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