[1]崔少华,李素文,黄金乐,等.改进的CNN用于单帧红外图像行人检测的方法[J].红外技术,2020,42(3):238-244.[doi:10.11846/j.issn.1001_8891.202003006]
 CUI Shaohua,LI Suwen,HUANG Jinle,et al.A Method of Pedestrian Detection Based on Improved CNN in Single-frame Infrared Images[J].Infrared Technology,2020,42(3):238-244.[doi:10.11846/j.issn.1001_8891.202003006]
点击复制

改进的CNN用于单帧红外图像行人检测的方法
分享到:

《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第3期
页码:
238-244
栏目:
出版日期:
2020-03-23

文章信息/Info

Title:
A Method of Pedestrian Detection Based on Improved CNN in Single-frame Infrared Images

文章编号:
1001-8891(2020)05-0238-07
作者:
崔少华李素文黄金乐单巍
淮北师范大学 物理与电子信息学院
Author(s):
CUI ShaohuaLI SuwenHUANG JinleSHAN Wei
College of Physics and Electronic Information, Huaibei Normal University
关键词:
图像处理LeNet-7系统单帧红外图像检测率
Keywords:
image processing LeNet-7 system single-frame infrared image detection rate
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.202003006
文献标志码:
A
摘要:
针对全卷积神经网络对单帧红外图像行人检测计算量大、检测率较低等问题,提出了一种改进的LeNet-7系统对红外图像行人检测的方法。该系统包含3个卷积层、3个池化层,通过错误率最小的试选法确定每层参数,以波士顿大学建立的BU-TIV数据库训练系统。首先,以俄亥俄州立大学建立的OTCBVS和Terravic Motion IR Database红外数据库作为测试图像;然后,采用自适应阈值的垂直和水平投影法得到感兴趣区域(regions of interest,ROI);最后,将得到的ROI输入训练好的系统进行测试。3个测试集检测实验表明,本文方法具有良好的识别能力,与不同实验方法相比,本文方法能有效提高检测率。
Abstract:
We proposed an improved method of pedestrian detection in infrared images based on the LeNet-7 system, to address the problems of large computation and low detection rates in traditional methods based on a full convolution neural network. The system consists of three convolution layers and three pooling layers. The trail selection method with the smallest error rate is used to determine the parameters of each layer, while the BU-TIV database, established by Boston University, is used to train the system. Firstly, the Object Tracking and Classification in and Beyond the Visible Spectrum(OTCBVS) and Terravic Motion IR Database, established by Ohio State University, are used to test images. Then, the region of interest (ROI) is obtained by vertical and horizontal projection with adaptive thresholds. Finally, the ROI is input into the trained system for testing. Experiments on three test sets demonstrate that the proposed method has good recognition ability. Compared with different experimental methods, the proposed method can effectively improve the detection rate.

参考文献/References:

[1] Nanda H , Davis L. Probabilistic template based pedestrian detection in infrared videos[C]//Intelligent Vehicle Symposium, IEEE, 2002: 7712599.
[2] Bertozzi M, Broggi A, Grisleri P, et al. Pedestrian detection in infrared images[C]//IEEE Intelligent Vehicles Symposium, IEEE, 2003: 7883392.
[3] GAO Y , AI X , WANG Y , et al. U-V-Disparity based Obstacle Detection with 3D Camera and steerable filter[C]//Proc Intl Intelligent Vehicle Symposium, IEEE, 2011: 12095161.
[4] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[5] 许茗, 于晓升, 陈东岳, 等. 复杂热红外监控场景下行人检测[J]. 中国图象图形学报, 2018, 23(12): 1829-1837.
XU M, YU X S, CHEN D Y, et al. Man detection in complex thermal infrared monitoring scenes[J]. Chinese Journal of Image and Graphics, 2018, 23(12): 1829-1837.
[6] 谭康霞, 平鹏, 秦文虎. 基于YOLO模型的红外图像行人检测方法[J]. 激光与红外, 2018, 48(11): 1436-1442.
TAN K X, PING P, QIN W H. Infrared image pedestrian detection method based on YOLO model[J]. Laser & Infrared, 2018, 48(11): 1436-1442.
[7] 陈恩加, 唐向宏, 傅博文. Faster R-CNN行人检测与再识别为一体的行人检索算法[J]. 计算机辅助设计与图形学学报, 2019, 31(2): 332-339.
CHEN E G, TANG X H, FU B W. Pedestrian Search Method Based on Faster R-CNN with the Integration of Pedestrian Detection and Re-identification[J]. Journal of Computer Aided Design & Graphics, 2019, 31(2): 332-339.
[8] 刘智嘉, 贾鹏, 夏寅辉, 等. 基于红外与可见光图像融合技术发展与性能评价[J]. 激光与红外, 2019, 49(5): 633-640.
LIU Z J, JIA P, XIA Y H, et al. Development and performance evaluation of infrared and visible image fusion technology[J]. Laser & Infrared, 2019, 49(5): 633-640.
[9] 吴志洋, 卓勇, 李军, 等. 基于卷积神经网络的单色布匹瑕疵快速检测算法[J]. 计算机辅助设计与图形学学报, 2018, 30(12): 2262-2270.
WU Z Y, ZHUO Y, LI J, et al. Fast detection algorithm of monochrome fabric defects based on convolution neural network[J]. Journal of Computer Aided Design & Graphics, 2018, 30(12): 2262-2270.
[10] 欧攀, 张正, 路奎, 等. 基于卷积神经网络的遥感图像目标检测[J]. 激光与光电子学进展, 2019, 56(5): 74-80.
OU P, ZHANG Z, LU K, et al. Remote sensing image target detection based on convolution neural network[J]. Laser & Optoelectronics Progress, 2019, 56(5): 74-80.
[11] Y. Lecun, L. Bottou, Y. Bengi, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[12] ZHENG Wu, Nathan Fuller, Diane Theriault, et al. IEEE Conference on Computer Vision and Pattern Recognition[DB/OL]. (2014-6-24) [2019-12-18]. http://csr.bu.edu/BU-TIV/BUTIV.html.
[13] 吕永标, 赵建伟, 曹飞龙. 基于复合卷积神经网络的图像去噪算法[J]. 模式识别与人工智能, 2017, 30(2): 97-105.
LU Y B, ZHAO J W, CAO F L. Image denoising algorithm based on compound convolution neural network[J]. Pattern Recognition & Artificial Intelligence, 2017, 30(2): 97-105.
[14] Riad I. Hammoud. OTCBVS Benchmark Dataset? Collection[DB/OL]. (2014-6-22)[2019-12-18]. http://vcipl-okstate.org/pbvs/bench/.
[15] Riad I. Hammoud. Terravic Motion IR Database[DB/OL]. (2014-6-22) [20192-12-18]. http://vcipl-okstate.org/pbvs/bench/Data/05/download. html.
[16] 苏育挺, 陈耀, 吕卫. 基于近红外图像的嵌入式人员在岗检测系统[J]. 红外技术, 2019, 41(4): 377-382.
SU Y T, CHEN Y, LU W. Embedded on-the-job detection system based on near infrared image[J]. Infrared Technology, 2019, 41(4): 377-382.
[17] XU Y L, MA B P, HUANG R, et al. Person search in a scene by jointly modeling people commonness and person uniqueness[C]//Proceedings of the 22nd ACM International Conference on Multimedia, 2014: 937-940.

相似文献/References:

[1]张双垒,林剑春,段东,等.基于遗传算法红外小目标检测的研究[J].红外技术,2012,34(08):472.
 ZHANG Shuang-lei,LIN Jian-chun,DUAN Dong,et al.Infrared Small Target Detection Research Based on Genetic Algorithm[J].Infrared Technology,2012,34(3):472.
[2]邢勇,邢冀川,宋艳.基于CCD的脉冲激光器远场发散角工程化研究[J].红外技术,2011,33(09):525.
 XING Yong,XING Ji-chuan,SONG Yan.Engineering Study on the CCD-based Measurement?of Pulse Laser far-field Divergence Angle[J].Infrared Technology,2011,33(3):525.
[3]李一芒,何 昕,魏仲慧.多级式红外预警图像处理系统设计与实现[J].红外技术,2014,36(2):131.[doi:10.11846/j.issn.1001_8891.201402009]
 LI Yi-mang,HE Xin,WEI Zhong-hui.Design and Implementation of Multistage Infrared Early Warning System[J].Infrared Technology,2014,36(3):131.[doi:10.11846/j.issn.1001_8891.201402009]
[4]许真,孙韶媛,代中华,等.基于纹理特征库的微光图像色彩纹理传递[J].红外技术,2011,33(01):049.
 XU Zhen,SUN Shao-yuan,DAI Zhong-hua,et al.Texture Library Based Color & Texture Transferring for LLL Images[J].Infrared Technology,2011,33(3):049.
[5]吴先权,华文深,赵莉君,等.红外上转换薄膜的分辨率数字化测试研究[J].红外技术,2010,32(11):632.
 WU Xian-quan,HUA Wen-shen,ZHAO Li-jun,et al.Study on Digitalized Resolution Testing of Infrared Up-conversion Thin Film[J].Infrared Technology,2010,32(3):632.
[6]代中华,孙韶媛,许真,等.一种车载红外视频彩色化算法[J].红外技术,2010,32(10):595.
 DAI Zhong-hua,SUN Shao-yuan,XU Zhen,et al.A Colorization Algorithm for Vehicle Infrared Video[J].Infrared Technology,2010,32(3):595.
[7]魏振忠,郭雨蓉,何小妹,等.基于蚁群算法和线段分析的建筑物特征提取[J].红外技术,2009,31(2):119.
 WEI Zhen-zhong,GUO Yu-rong,HE Xiao-mei,et al.The Extraction of Building Based on Ant Colony Algorithm?and the Analysis of Line Segment[J].Infrared Technology,2009,31(3):119.
[8]胡茂海,符建辉,陶纯堪.基于DSP实时联合变换相关器系统设计[J].红外技术,2009,31(4):196.
 HU Mao-hai,FU Jian-hui,TAO Chun-kan.The System Design of Real-time Joint Transform Correlator Based on DSP[J].Infrared Technology,2009,31(3):196.
[9]孙君顶,赵慧慧.图像稀疏表示及其在图像处理中的应用[J].红外技术,2014,36(7):533.[doi:10.11846/j.issn.1001_8891.201407004]
 SUN Jun-ding,ZHAO Hui-hui.Sparse Representation and Applications in Image Processing[J].Infrared Technology,2014,36(3):533.[doi:10.11846/j.issn.1001_8891.201407004]
[10]薛 军,邹建华,张永亮.红外跟踪测量系统图像处理电路的设计[J].红外技术,2014,36(8):652.[doi:10.11846/j.issn.1001_8891.201408010]
 XUE Jun,ZOU Jian-hua,ZHANG Yong-liang.Design of Image-processing Circuit in Infrared Tracking and Measuring System[J].Infrared Technology,2014,36(3):652.[doi:10.11846/j.issn.1001_8891.201408010]

备注/Memo

备注/Memo:
收稿日期:2019-06-25;修订日期:2019-12-18.
作者简介:崔少华(1983-),女,硕士,讲师,主要从事信号去噪、图像处理等方面的研究。E-mail:flower0804@126.com。
基金项目:国家自然科学基金面上项目(41875040);安徽省教育厅项目(2018jyxm0530,2017kfk044,KJ2017B008)。

更新日期/Last Update: 2020-03-17