Indian Journal of Geo-Marine Sciences

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Sea-surface object detection scheme for USV under foggy environment


Affiliations
1 College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing – 400 074, China
2 College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin – 150 001, China

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.
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  • Sea-surface object detection scheme for USV under foggy environment

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Authors

T Zhang
College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing – 400 074, China
X Liu
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin – 150 001, China
M Zhang
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin – 150 001, China

Abstract


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.