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

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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

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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"

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