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Sea-surface object detection scheme for USV under foggy environment

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Title Sea-surface object detection scheme for USV under foggy environment
 
Creator Zhang, T
Liu, X
Li, Y
Zhang, M
 
Subject Anti-fog enhancement
Detection accuracy
Sea-surface target
Target detection network
 
Description 960-968
Sea-surface target detection is investigated for the visual image-based autonomous control of an Unmanned Surface
Vessel (USV). A traditional way is to dehaze for sea-surface images in the previous target detection algorithms. However, it
would cause a problem that the image dehaze performance and detection speed are difficult to be balanced. To solve the
above problem, a YOLO (You Only Look Once) based target detection network with good anti-fog ability is proposed for
sea-surface target detection. In this proposed method, the target detection network is trained off-line to obtain a good antifog
ability and the target detection is performed on-line. A hazed sample generation model is built based on atmospheric
single scattering inverse model to obtain sufficient samples for the off-line training in the proposed method. And then, the
target detection network is trained based on the generated samples to obtain good anti-fog ability according to a new
learning strategy. Finally, comparative experimental results demonstrate the effectiveness of the proposed target detection
algorithm.
 
Date 2022-03-14T09:54:55Z
2022-03-14T09:54:55Z
2021-11
 
Type Article
 
Identifier 2582-6727 (Online); 2582-6506 (Print)
http://nopr.niscair.res.in/handle/123456789/59321
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJMS Vol.50(11) [November 2021]