In this paper, a water-target recognition algorithm based on the data acquired by the onboard visual sensor from unmanned surface vessels (USV) is reported, in order to satisfy the accuracy and speed requirements of USV intelligent sensing system. The main outcome are summarized as follows: First, images are collected based on open source datasets and experimental data, to create a watertarget recognition database which named YZ10K; second, popular deep-learning based target detection methods including Faster RCNN, SSD, YOLOv3, etc. are implemented and compared; third, based on the characteristics of water targets, an enhanced lightweight Water Target detection network WT-YOLO (water target-YOLO) is proposed. The experimental verification shows that the WT-YOLO algorithm based on improved YOLOv3 has achieved accurate and real-time target recognition with the mean average precision (mAP) of 79. 30% and frame per second of 30. 01.