哈尔滨工程大学学报2023，Vol.55Issue(10)：1832-1840,9.DOI:10.11990/jheu.202204041
基于更快区域卷积神经网络的多视角船舶识别
Multiview ship recognition based on the faster region convolutional neural network
摘要
Abstract
The appearance of ships varies greatly under different views,and the complex ocean environment increases the difficulty of obtaining multiview ship images.In this paper,the self-made multiview images of different types of ships were used as datasets to train the faster region convolutional neural network(faster R-CNN)model.Then,we evaluated the recognition performance of the faster R-CNN model for ships of different views by using the mean F1 score,mean accuracy,and mean log-average miss rate as evaluation indexes.Furthermore,the recognition ability of the faster R-CNN to different levels of quality and background images was analyzed by identifying the F1 scores and log-average miss rates of different ships.The experimental results showed that the mean F1 score,mean accuracy,and mean log-average miss rate of the faster R-CNN were 0.696 9,92.88%and 8.34%,respectively.The faster R-CNN has high recognition ability when processing multiview ship images;however,the same ability is significantly reduced when processing low-pixel images in foggy or dim environments.关键词
多视角/船舶识别/视觉图像/更快区域卷积神经网络/目标检测/特征提取/深度学习/低分辨率图像Key words
multiview/ship recognition/vision image/faster region convolutional neural network(faster R-CNN)/object detection/feature extraction/deep learning/low resolution image分类
信息技术与安全科学引用本文复制引用
程静,王荣杰,曾光淼,林安辉,王亦春..基于更快区域卷积神经网络的多视角船舶识别[J].哈尔滨工程大学学报,2023,55(10):1832-1840,9.基金项目
国家自然科学基金项目(51879118). （51879118）
