海上船艇检测是无人水面艇(unmanned surface vehicle,USV)视觉系统最主要的任务之一. 提出了一种基于目标候选(objectproposal)的 USV 海上船艇检测算法. 首先, 基于改进的边缘框(edgebox)算法提取了图像的边缘信息,并建立"目标性"评分函数获取目标候选框; 然后,对船艇目标进行方向梯度直方图(histogram of oriented gradient,HOG)特征进行建模, 利用支持向量机(support vector machine, SVM),采用"自举法"训练分类器进行迭代; 最后,将目标候选框的特征描述子输入到分类器中, 对船艇进行检测. 此外,基于 USV 在海天环境下的运行场景,结合海天线的特性进一步提升算法的检测性能. 实验结果表明,该算法能快速、准确地检测船艇目标, 且具有较高的检测率,对尺度以及光照条件的变化也有较强的鲁棒性.

Maritime ships detection is one of the main tasks in unmanned surface vehicle (USV)'s visual system. This paper proposes a kind of USV maritime ships detection algorithm based on object proposals. Firstly, a modified edge boxes algorithm is utilized to extract the edge information of the image, and an objectness score function is established to obtain object proposals. Secondly, a histogram of oriented gradient (HOG) feature model is built for the ship, and the support vector machine (SVM) is utilized to iteratively train a classifier by a bootstrap method. Finally, the feature descriptor of object proposals is fed into the classifier, and detecting the ship. In addition, the sea-sky line is utilized to further improve the detection performance of the algorithm based on the environment of USV. The experimental results show that the algorithm can rapidly and accurately detect the ship on the sea, and achieve a relatively high detection rate. And the algorithm has strong robustness to the change of the scale and the illumination conditions.