基于目标候选的USV海上船艇检测  

Maritime ships detection for USV based on object proposals

作　　者：陈伟[1] 杨毅[1] 李小毛[1] 刘远 张鑫[1] CHEN Wei;YANG Yi;LI Xiaomao;LIU Yuan;ZHANG Xin(School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学机电工程与自动化学院

出　　处：《上海大学学报（自然科学版）》2019年第5期668-678,共11页Journal of Shanghai University:Natural Science Edition

基　　金：国家自然科学基金资助项目(61673254,61403245,51675378);上海市科委地方院校能力建设资助项目(14500500400)

摘　　要：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(object proposal)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.

关 键 词：目标候选 无人水面艇 海上船艇 目标检测 边缘框 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]