Sea-surface object detection scheme for USV under foggy environment

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 anti-fog 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.