吉林大学学报(工学版) ›› 2019, Vol. 49 ›› Issue (5): 1661-1667.doi: 10.13229/j.cnki.jdxbgxb20180642

• • 上一篇    

基于深度学习的行人和车辆检测

徐谦1,2(),李颖1,2,王刚1,2()   

  1. 1. 吉林大学 计算机科学与技术学院，长春 130012
    2. 吉林大学 符号计算与知识工程教育部重点实验室，长春 130012
  • 收稿日期:2018-06-19 出版日期:2019-09-01 发布日期:2019-09-11
  • 通讯作者: 王刚 E-mail:xuqian16@jlu.edu.cn;wanggang.jlu@gmail.com
  • 作者简介:徐谦(1990-),男,博士研究生.研究方向:计算机视觉.E-mail:xuqian16@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61602206)

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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摘要：

为解决自动驾驶行车环境目标检测的问题，提出了一种基于深度学习的行人和车辆检测网络PVDNet。在网络底层，改进了跳跃连接结构，提出多级跳跃连接MLSC，加速了模型的收敛速度和收敛精度；在网络顶层，设计了一种多层特征融合方法MLFF，将底层特征与顶层特征融合以提高检测精度；在网络输出层，提出了一种单维卷积方法ODC替代全连接层，减少了模型参数以提高检测速度。实验表明：与原始的Faster R-CNN相比，PVDNet在数据集PascalVOC2007、PascalVOC2012、MS COCO、KITTI上行人和车辆平均检测准确率分别提高了3.7%、6.1%、5.6%、9.62%。

关键词: 人工智能, 目标检测, 深度学习, 无人驾驶

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

中图分类号: 

  • TP301.6

图1

Faster R-CNN网络整体架构"

图2

PVDNet整体结构"

图3

单维卷积"

表1

各测试集上的行人和车辆检测结果"

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

图4

测试集上PVDNet检测效果图"

表2

TitanX上模型检测速度对比"

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

图5

PVDNet在测试集上预测错误效果图"

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