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一种改进的HED网络及其在边缘检测中的应用 期刊论文
红外技术, 2019, 卷号: 41, 期号: 1, 页码: 72-77
Authors:  焦安波;  何淼;  罗海波
View  |  Adobe PDF(787Kb)  |  Favorite  |  View/Download:83/12  |  Submit date:2019/03/08
深度学习  HED网络  红外图像  显著性边缘  边缘检测  
Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model 期刊论文
IEEE Access, 2019, 卷号: 7, 页码: 89475-89486
Authors:  He M(何淼);  Luo HB(罗海波);  Hui B(惠斌);  Chang Z(常铮)
View  |  Adobe PDF(19186Kb)  |  Favorite  |  View/Download:112/21  |  Submit date:2019/08/04
Online, pedestrian detection  multi-object tracking  re-identifying  Kalman lter  data association  
A Simple and Efficient Network for Small Target Detection 期刊论文
IEEE Access, 2019, 卷号: 7, 页码: 85771-85781
Authors:  Ju MR(鞠默然);  Luo, Jiangning;  Zhang PP(张盼盼);  He M(何淼);  Luo HB(罗海波)
View  |  Adobe PDF(13403Kb)  |  Favorite  |  View/Download:176/32  |  Submit date:2019/08/04
Deep learning   target detection   passthrough layer   dilated convolution  
Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter 期刊论文
APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 8, 页码: 1-13
Authors:  He M(何淼);  Luo HB(罗海波);  Hui B(惠斌);  Chang Z(常铮)
View  |  Adobe PDF(27079Kb)  |  Favorite  |  View/Download:117/18  |  Submit date:2019/06/29
pedestrian flow statistics  neural network  Kalman filter  multi-object tracking  data association  
改进的YOLO V3算法及其在小目标检测中的应用 期刊论文
光学学报, 2019, 卷号: 39, 期号: 7, 页码: 1-8
Authors:  鞠默然;  罗海波;  王仲博;  何淼;  常铮;  惠斌
View  |  Adobe PDF(2348Kb)  |  Favorite  |  View/Download:663/91  |  Submit date:2019/04/13
机器视觉  小目标检测  YOLO V3  VEDAI数据集  K-means  
基于双模全卷积网络的行人检测算法 期刊论文
红外与激光工程, 2018, 卷号: 47, 期号: 2, 页码: 10-17
Authors:  罗海波;  何淼;  惠斌;  常铮
View  |  Adobe PDF(800Kb)  |  Favorite  |  View/Download:169/25  |  Submit date:2018/03/25
深度学习  弱监督训练  行人检测  语义分割  
Pedestrian detection with semantic regions of interest 期刊论文
Sensors (Switzerland), 2017, 卷号: 17, 期号: 11, 页码: 1-16
Authors:  He M(何淼);  Luo HB(罗海波);  Chang Z(常铮);  Hui B(惠斌)
View  |  Adobe PDF(9871Kb)  |  Favorite  |  View/Download:186/27  |  Submit date:2017/12/11
Pedestrian Detection  Deep Learning  Background Vs. Foreground Errors  Semantic Regions Of Interest