基于弱显著图的实时热红外图像行人检测

李传东; 徐望明; 伍世虔

基于弱显著图的实时热红外图像行人检测

李传东^{1, 2},
徐望明^{1, 2, 3, ,},
伍世虔^{1, 2}

1.
武汉科技大学信息科学与工程学院，湖北武汉 430081
2.
武汉科技大学机器人与智能系统研究院，湖北武汉 430081
3.
武汉科技大学教育部冶金自动化与检测技术工程研究中心，湖北武汉 430081

基金项目:

国家自然科学基金 61775172

湖北省教育厅科研计划资助项目 D20191104

教育部产学合作协同育人项目 201902303039

详细信息

作者简介:
李传东（1995-），男，湖北黄冈人，硕士研究生，研究方向为图像处理、目标检测及深度学习

通讯作者:
徐望明（1979-），男，湖北武汉人，博士，高级工程师，研究方向为图像处理与模式识别。E-mail: xuwangming@wust.edu.cn

中图分类号: TP391.41
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- 被引次数: 0
出版历程
- 收稿日期: 2020-09-24
- 修回日期: 2020-11-03
- 刊出日期: 2021-07-01

Real-Time Pedestrian Detection Based on the Weak Saliency Map in Thermal Infrared Images

LI Chuandong^{1, 2},
XU Wangming^{1, 2, 3
, ,},
WU Shiqian^{1, 2}

1.
School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
2.
Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China
3.
Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China

摘要

摘要: 针对现有热红外图像行人检测方法在精度和速度方面存在的问题，提出一种基于弱显著图的实时行人检测方法。该方法以轻量级LFFD（Light and Fast Face Detector）网络为基础，由两级改进网络即SD-LFFD（Saliency Detection-LFFD）和SF-LFFD（Saliency Fusion-LFFD）组成，首先以热红外图像作为输入经SD-LFFD网络产生初步行人检测结果和行人区域弱显著图，接着将该弱显著图与原热红外图像结合“点亮”潜在行人区域并经SF-LFFD网络产生新的行人检测结果，最后将两级改进网络的行人检测结果融合得到最终结果。在数据集CVC-09和CVC-14上实验结果表明，该方法与现有轻量级神经网络相比行人检测的平均精确率有大幅提升，且在有限硬件资源下可实现实时检测。
- 热红外图像 /
- 行人检测 /
- 显著性检测 /
- 实时检测
Abstract: To address the low precision and speed of existing pedestrian detection methods for thermal infrared images, a real-time pedestrian detection method based on a weak saliency map is herein proposed. The proposed method comprises two improved networks, namely, SD-LFFD and SF-LFFD, which use lightweight LFFD as the basic network. First, the thermal infrared image is input into the SD-LFFD to produce the preliminary pedestrian detection results and a weak saliency map indicating the pedestrian regions. Then, the weak saliency map and the original thermal infrared image are combined to highlight the potential pedestrian regions and generate new results using the SF-LFFD. Finally, the pedestrian detection results obtained by the two improved networks are integrated to obtain the final results. The experimental results on the CVC-09 and CVC-14 datasets indicate that the proposed method significantly improves the average precision (AP) of pedestrian detection compared with that of existing lightweight neural networks, and that it achieves real-time detection with limited hardware resources.
- thermal infrared image /
- pedestrian detection /
- saliency detection /
- real-time detection

HTML全文

图 1 LFFD网络结构图

Figure 1. The network structure of LFFD

下载: 全尺寸图片幻灯片

图 2 本文方法的工作流程

Figure 2. Flowchart of the proposed method

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图 3 显著性检测网络结构

Figure 3. The structure of saliency detection network

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图 4 显著图标签

Figure 4. Saliency map label

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图 5 两个数据集上行人检测结果的P-R曲线

Figure 5. The P-R curves of pedestrian detection results on two datasets

下载: 全尺寸图片幻灯片

图 6 本文方法与Tiny-YOLO v3方法的AP值对比

Figure 6. AP comparison between the proposed method and Tiny- YoLov3 method

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表 1 CVC-09和CVC-14数据集的样本分布

Table 1. The distribution of samples in CVC-09 and CVC-14

Dataset	Day		Night
Dataset	Train set	Test set	Train set	Test set
CVC-09	4225	2882	3201	2883
CVC-14	3695	707	3390	727

下载: 导出CSV

表 2 行人检测AP值比较

Table 2. AP comparison for pedestrian detection %

Dataset	TestScenario	AP(IoU＝0.5)
Dataset	TestScenario	ORI-LFFD	SD-LFFD	SF-LFFD	SD-LFFD+SF-LFFD
CVC-09	Day	74.15	73.25	76.05	78.46
	Night	74.70	75.54	75.81	79.85
	Total	73.82	74.01	75.52	78.74
CVC-14	Day	53.94	57.93	64.81	66.76
	Night	75.70	76.17	83.61	83.94
	Total	63.45	66.06	73.21	74.46

下载: 导出CSV

表 3 行人检测的速度对比

Table 3. Speed comparison for pedestrian detection

Method	Model size/M	Frame rate /fps	Inference speed/ms
Tiny-YOLOv3	33.99	18.31	54.61
SD-LFFD+SF-LFFD	14.45	31.25	32

下载: 导出CSV

参考文献(14)

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