The proposed model achieves 73.5% AP on target detection in our millimeter wave images dataset while keeps 35 frames per seconds for real time detection.

The proposed model achieves 73.5% AP on target detection in our millimeter wave images dataset while keeps 35 frames per seconds for real time detection. CCS ...

The GLNet consists of two convolution neural networks: a global faster and coarse region proposal network (GNet) aiming to extract potential regions containing ...

GLNet for Target Detection in Millimeter Wave Images. https://doi.org/10.1145 ... Automatic detection of concealed pistols using passive millimeter wave imaging ...

基于CNN(卷积神经网络)，本文提出了一种全局和局部网络，即GLNet。GLNet由两个卷积神经网络组成:一个是用于提取包含目标的潜在区域的全局快速粗糙区域建议网络(GNet)，另 ...

This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection.

A method that combines image processing and statistical machine learning techniques to solve the localization/detection problem of passive Millimeter Wave ...

In [48], the authors have used a global and local network. (GLNet) to detect hidden objects on AMMW radar images. The GLNet pipeline consists of a coarse region ...

This paper describes an anomaly target detection algorithm based on JPEG images. The algorithm deals directly with JPEG image data eliminating JPEG image ...