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基于改进Mask R-CNN的可见光图像中舰船目标检测方法

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基于改进Mask R-CNN的可见光图像中舰船目标检测方法
Ship Target Detection in Optical Images Based on Improved Mask R-CNN
投稿时间：2020-05-05
DOI：10.15918/j.tbit1001-0645.2020.076
中文关键词:舰船目标目标检测Mask R-CNN语义分割
English Keywords:ship targetstarget detectionMask R-CNNsemantic segmentation
基金项目:国家自然科学基金资助项目（61471412，61771020）
作者单位E-mail
马啸海军研究院, 北京 100071
邵利民海军大连舰艇学院 航海系, 辽宁, 大连 116000shaolimin@sohu.com
金鑫海军大连舰艇学院 航海系, 辽宁, 大连 116000
卢惠民国防科技大学 智能科学学院 自动化系, 湖南, 长沙 410000
肖军浩国防科技大学 智能科学学院 自动化系, 湖南, 长沙 410000
谷东亮海军大连舰艇学院 航海系, 辽宁, 大连 116000
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中文摘要:
针对基于卷积神经网络的目标识别方法中经典的矩形检测框在检测舰船目标时会框出很多无关区域，易出现漏检、误检等问题，提出基于改进Mask R-CNN （mask region-based convolution neural networks）的舰船目标检测方法，在Mask R-CNN网络的基础上通过增加判别模块、类别预测分支和语义分割分支对视觉系统采集的可见光图像中的舰船目标进行目标定位和类别预测，同时获得舰船目标的边缘轮廓并实现对军舰目标的语义分割，为海上无人作战系统提供更精确的信息.实验结果表明，该方法在保持较高检出率和运行效率的同时误检率较低，舰船目标的平均检测精度较高，具有良好的舰船目标检测性能.
English Summary:
A ship target detection method was proposed based on improved Mask R-CNN (mask region-based convolution neural networks) to solve the problem that the classical rectangular detection boxes within the typical target detection algorithms based on the convolutional neural network will frame many irrelevant areas in ship target detecting, which can lead to problems such as missed detection and false detection. On the basis of Mask R-CNN, a discrimination module, category prediction branch, and semantic segmentation branch were added, thus, the target localization and category prediction of the ship targets in the optical images collected by the vision system were performed. At the same time, the edge contour of the ship target was obtained and semantic segmentation of warship targets was realized, providing more accurate information for the maritime unmanned combat system. Experimental results show that, keeping a high detection rate and operation efficiency, the proposed method can achieve lower false detection rate, higher average accuracy of ship target detection, better ship target detection performance.
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