激光与光电子学进展, 2019, 56 (13): 131009, 网络出版: 2019-07-11
基于改进Faster RCNN的毫米波图像实时目标检测 下载: 992次
Real-Time Object Detection for Millimeter-Wave Images Based on Improved Faster Regions with Convolutional Neural Networks
图 & 表
图 2. 毫米波图像与光学图像对比。(a)毫米波图像,图中方框为真实标记;(b)光学图像,图中方框为检测结果,数字为目标置信度
Fig. 2. Comparison between millimeter wave images and optical images. (a) Millimeter wave images, boxes in figure are real markers; (b) optical images, boxes in figure are the test results, and the numbers are the target confidence
图 4. 两个数据集中标记框的面积统计图。(a)毫米波图像数据集的统计图;(b) VOC数据集的统计图
Fig. 4. Area statistics of label boxes in two data sets. (a) Statistical chart of millimeter wave image data sets; (b) statistical chart of VOC data sets
表 1特征提取网络结构
Table1. Feature extraction network structure
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表 2网络测试GPU环境为GTX 1080时的实验结果对比
Table2. Comparison of experimental results when the GPU environment for network testing is GTX 1080
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侯冰基, 杨明辉, 孙晓玮. 基于改进Faster RCNN的毫米波图像实时目标检测[J]. 激光与光电子学进展, 2019, 56(13): 131009. Bingji Hou, Minghui Yang, Xiaowei Sun. Real-Time Object Detection for Millimeter-Wave Images Based on Improved Faster Regions with Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131009.