摘要
目的遥感图像上任意方向舰船目标的检测,是给出舰船在图像上的最小外切矩形边界框。基于双阶段深度网络的任意方向舰船检测算法速度较慢;基于单阶段深度网络的任意方向舰船检测算法速度较快,但由于舰船具有较大长宽比的形态特点,导致虚警率较高。为了降低单阶段目标检测的虚警率,进一步提升检测速度,针对舰船目标的形态特点,提出了基于密集子区域切割的快速检测算法。方法沿长轴方向,将舰船整体密集切割为若干个包含在正方形标注框内的局部子区域,确保标注框内最佳的子区域面积有效占比,保证核心检测网络的泛化能力;以子区域为检测目标,训练核心网络,在训练过程对重叠子区域进行整合;基于子图分割将检测得到的子区域进行合并,进而估计方向角度等关键舰船目标参数。其中采用子区域合并后处理替代了非极大值抑制后处理,保证了检测速度。结果在HRSC2016(high resolution ship collections)实测数据集上,与最新的改进YOLOv3(you only look once)、RRCNN(rotated region convolutional neural network)、RRPN(rotation region proposal networks)、RDFPN-3(rotation dense feature pyramid network)和R-DFPN-4等5种算法进行了比较,相较于检测精度最高的R-DFPN-4对照算法,本文算法的m AP(mean average precision)(IOU(inter section over union)=0.5)值提高了1.9%,平均耗时降低了57.9%;相较于检测速度最快的改进YOLOv3对照算法,本文算法的m AP (IOU=0.5)值提高了3.6%,平均耗时降低了31.4%。结论本文所提出的任意方向舰船检测算法,结合了舰船目标的形态特点,在检测精度与检测速度均优于当前主流任意方向舰船检测算法,检测速度有明显提升。
Objective Ship detection based on remotely sensed images aims to locate ships,which is of great significance in national water surveillance and territorial security. The rectangular bounding boxes for target location in the typical deep learning method are usually in the horizontal-vertical direction,whereas the distribution of ships on remotely sensed images is arbitrarily oriented or in varying directions. For narrow and long ships with arbitrary directions,the vertical-horizontal bounding box is fairly rough. When the ship deviates from the vertical or horizontal direction,the bounding box is inaccurate,and the bounding box has many nonship pixels. If multiple ships are close to one another on the image,several ships may not be located because they are overlapped by the bounding boxes of the neighboring ships. Therefore,using a finer bounding box in detection is beneficial for detecting ship targets,and more precise ship positioning information is helpful for subsequent ship target recognition. For this reason,the classical deep-learning-based target detection is extended,and a finer minimum circumscribed rectangular bounding box is utilized to locate the ship target. Existing extended detection algorithms can be divided into two categories: one-stage detection and two-stage detection. One-stage detection directly outputs the target’s location estimation,whereas two-stage detection classifies the proposed regions to eliminate the false targets.The disadvantage of two-stage detection is its slower speed. One-stage detection is faster,but its false alarm rate is higher for narrow and long ships. A fast detection algorithm based on dense sub-region segmentation is proposed according to the shape characteristics of ship targets to reduce the false alarm rate of one-stage detection and further improve the detection speed. Method The basic idea of our algorithm is to segment a ship into several sub-regions on which detection and combination are carried out,according to the long and narrow shape characteristic of s
作者
陈华杰
吴栋
谷雨
Chen Huajie;Wu Dong;Gu Yu(Key Laboratory of Fundamental Science for National Defense-Communication Information Transmission and Fusion Technology Laboratory,Hangzhou Dianzi University,Hangzhou 310018,China)
出处
《中国图象图形学报》
CSCD
北大核心
2021年第3期654-662,共9页
Journal of Image and Graphics
基金
国防基础科研项目(JCKY2018415C004)
国防科技重点实验室基金项目(6142804180407)
省级重点研发项目(2019C0505)。
关键词
任意方向舰船检测
密集子区域切割
子图分割
子区域合并
快速检测
arbitrary direction ship detection
dense sub-region segmentation
sub-graph segmentation
sub-region merging
fast detection
