Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1661-1667.doi: 10.13229/j.cnki.jdxbgxb20180642

Previous Articles    

Pedestrian-vehicle detection based on deep learning

Qian XU1,2(),Ying LI1,2,Gang WANG1,2()   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2018-06-19 Online:2019-09-01 Published:2019-09-11
  • Contact: Gang WANG E-mail:xuqian16@jlu.edu.cn;wanggang.jlu@gmail.com

RICH HTML

 61   

PDF (PC)

368

Abstract:

A pedestrian-vehicle detection network (PVDNet) is presented for pedestrian and vehicle detection in driving environment based on deep learning. First, on the low layers, an improved skip connection called Multi-Level Skip Connection (MLSC) is proposed to accelerate the convergence speed and the accuracy of the model. Second, on the top layers, a Multi-Layer Features Fusion (MLFF) method is designed to improve the detection accuracy by combining the low-level features with the high-level features. Finally, on the output layer, an One-Dimensional Convolution (ODC) Method is proposed to reduce the model parameters and improve the detection speed by replacing the fully connection layer. Experiments of the proposed PVDNet were carried out on the PascalVOC2007, PascalVOC2012, MS COCO, KITTI datasets. results show that, compared with the original Faster R-CNN, the mean average detection accuracies on the PascalVOC2007, PascalVOC2012, MS COCO, KITTI datasets are promoted 3.7%, 6.1%, 5.6%, 9.62% respectively by using PVDNet.

Key words: artificial intelligence, object detection, deep learning, self-driving

CLC Number: 

  • TP301.6

Fig. 1

Architecture of Faster R-CNN"

Fig.2

Architecture of PVDNet"

Fig.3

One-dimension convolution"

Table 1

Detection results of pedestrians and vehicles on test sets"

数据集 FasterR-CNN YOLO SSD(300) PVDNet

Pascal

VOC2007

 行人AP 76.7 57.3 74.5 81.4
车辆AP 84.5 65.4 80.8 87.2
MAP 80.60 61.35 77.65 84.30

Pascal

VOC2012

 行人AP 79.6 63.5 77.5 84.3
车辆AP 76.4 55.8 74.7 83.9
MAP 78.00 59.65 76.10 84.10
MS COCO 行人AP 57.9 36.2 53.6 62.7
车辆AP 67.5 51.2 62.4 73.9
MAP 62.70 43.70 58.00 68.30
KITTI 行人AP 65.91 24.35 88.69 85.32
车辆AP 79.11 35.86 66.41 78.93
MAP 72.51 30.11 77.55 82.13

Fig.4

Detection results of PVDNet on test sets"

Table 2

Detection speed of models on TitanX"

算 法 帧率/FPS
Faster R-CNN 7
YOLO 155
SSD(300) 58
PVDNet 25

Fig.5

Wrong detection results of PVDNet on test sets"

1 曲昭伟, 魏福禄, 魏巍, 等 . 雷达与视觉信息融合的行人检测方法[J]. 吉林大学学报: 工学版, 2013, 43(5): 1230-1234.
Qu Zhao-wei , Wei Fu-lu , Wei Wei , et al . Pedestrian detection by radar vision data fusion[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(5): 1230-1234.
2 Park K , Kim S , Sohn K . Unified multi-spectral pedestrian detection based on probabilistic fusion networks[J]. Pattern Recognition, 2018, 80: 143-155.
3 Zhang X W , Cheng L , Li B , et al . Too far to see? not really!—pedestrian detection with scale-aware localization policy[J]. IEEE Transactions on Image Processing, 2017, 27(8): 3703-3715.
4 李琳辉, 伦智梅, 连静, 等 . 基于卷积神经网络的道路车辆检测方法[J]. 吉林大学学报: 工学版, 2017, 47(2): 384-391.
Li Lin-hui , Zhi-mei Lun , Lian Jing , et al . Convolution neural network-based vehicle detection method[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(2): 384-391.
5 Karaimer H C , Baris I , Bastanlar Y . Detection and classification of vehicles from omnidirectional videos using multiple silhouettes[J]. Pattern Analysis and Applications, 2017, 20(3): 893-905.
6 Ershadi N Y , Menendez J M , Jimenez D . Robust vehicle detection in different weather conditions: using MIPM[J/OL]. [2018-06-19]. https:⫽journals.plos.org/plosone/article?id=10.1371/journal.pone.0191355
7 Girshick R , Donahue J , Darrell T , et al . Region based convolutional networks for accurate object detection and segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(1): 142-158.
8 He K , Zhang X , Ren S , et al . Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(9): 1904-1916.
9 Girshick R . Fast R-CNN[C]⫽International Conference on Computer Vision, Santiago, Chile, 2015: 1440-1448.
10 Ren S Q , He K M , Girshick R , et al . Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
11 Redmon J , Divvala S , Girshick R , et al . You only look once: unified, real-time object detection[C]⫽IEEE Computer Vision and Pattern Recognition, Las Vegas, Nevada, 2016: 779-788.
12 Liu W , Anguelov D , Erhan D , et al . SSD: single shot multibox detector[C]⫽European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21-37.
13 Shelhamer E , Long J , Darrell T . Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.
14 Liu W , Anguelov D , Erhan D , et al . SSD: single shot multibox detector[EB/OL]. [2018-09-11]. https:⫽github.com/weiliu89/caffe/tree/ssd
15 Ren S Q , He K M , Girshick R , et al . Faster R-CNN (python implementation)[EB/OL]. [2018-09-11]. https:⫽github.com/rbgirshick/pyfaster-rcnn
16 Redmon J , Divvala S , Girshick R , et al . YOLO: real-time object detection[EB/OL]. [2018-09-11]. https:⫽pjreddie.com/darknet/yolo/
[1] Wan-fu GAO,Ping ZHANG,Liang HU. Nonlinear feature selection method based on dynamic change of selected features [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1293-1300.
[2] Li⁃min GUO,Xin CHEN,Tao CHEN. Radar signal modulation type recognition based on AlexNet model [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 1000-1008.
[3] Dan⁃tong OUYANG,Jun XIAO,Yu⁃xin YE. Distant supervision for relation extraction with weakconstraints of entity pairs [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 912-919.
[4] GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585.
[5] DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.
[6] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[7] ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[8] LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[9] HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[10] LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[11] LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[12] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[13] YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[14] YANG Chao-yu, LI Ce, LIANG Yin-cheng, YANG Feng. Blurred object detection based on improved particle filter in coal mine underground surveilance [J]. 吉林大学学报(工学版), 2017, 47(6): 1976-1985.
[15] LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd