摘要
近年来,随着高分辨率遥感影像和船舶智能化的发展,通过遥感技术在大范围内对船舶目标进行检测识别已在海洋监管和安全等领域发挥出重要的现实意义。考虑到人类的视觉回路系统中对外界特定目标有很强的方向选择性,借鉴视觉的方向选择性机制,将有助于提升舰船检测识别任务的性能。从3个方面来模拟这种视觉的方向性选择机制:对卷积层采用Gabor卷积核分解的方法来模拟视觉回路的方向性,使深度卷积网络具有方向不变性;通过采用方向回归的方式估计舰船目标的主方向,模拟方向性选择机制;结合方向性目标来提升舰船检测识别任务的性能。试验结果表明:与快速区域卷积神经网络(Faster R-CNN)、单步多框检测(SSD)和定向响应网络(ORN)方法相比,该方法能取得较好的效果,表现出潜在的优势,均值平均精度(mAP)可达到约98%。
In recent years,with the development of high-resolution remote sensing images and ship intelligence,the detection and identification of ship targets through remote sensing technology in a wide range has played an important role in the field of marine supervision and safety,etc.Considering that the human visual circuit system has a strong directional selectivity towards specific targets in the outside world,drawing on the directional selectivity mechanism of vision will help to improve the performance of the detection and identification task of ships.We simulate the direction-selective mechanism of vision in three ways:we use the Gabor convolutional kernel decomposition of the convolutional layer to simulate the directionality of visual circuits,so as to make the deep convolutional neural network directionally invariant;we simulate the direction-selective mechanism by estimating the main direction of ship targets through directional regression;and we combine the directional target with the direction-selective target to improve the performance of ship detection and identification task.The results of the tests showed that compared with Faster R-CNN(Faster Region Convolution Neural Network),SSD(Single Shot multibox Detector)and ORN(Oriented Response Network)methods,this method can achieve good results and show potential advantages,and the mAP(mean Average Precision)can reach about 98%.
作者
徐红明
王兴华
方诚
徐昕辉
XU Hongming;VWANG Xinghua;FANG Cheng;XU Xinhui(Maritime Department,Zhejiang Institute of Communications,Hangzhou 311112,China;Navigation College,Jimei University,Xiamen 361021,China;State Key Lab of CAD&CG,Zhejiang University,Hangzhou 310058,China)
出处
《中国航海》
CSCD
北大核心
2024年第2期120-127,共8页
Navigation of China
基金
浙江省基础公益研究计划项目(LGG18E090001)
浙江省博士后科研项目(225846)
国家自然科学基金(51879119)
福建省自然科学基金(2022J01323)。
关键词
舰船遥感
目标检测
舰船识别
深度卷积网络
ship remote sensing
target detection
ship identification
deep convolutional neural network
