吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (4): 1427-1436.doi: 10.13229/j.cnki.jdxbgxb20200588

• 计算机科学与技术 • 上一篇    

基于改进YOLOv3的车辆前方动态多目标检测算法

金立生1,2(),郭柏苍1,王芳荣3,石健4()   

  1. 1.燕山大学 车辆与能源学院，河北 秦皇岛 066004
    2.燕山大学 河北省特种运载装备重点实验室，河北 秦皇岛 066004
    3.吉林大学 通信工程学院，长春 130022
    4.北京理工大学 机械与车辆学院，北京 100081
  • 收稿日期:2020-07-31 出版日期:2021-07-01 发布日期:2021-07-14
  • 通讯作者: 石健 E-mail:jinls@ysu.edu.cn;sj920443563@126.com
  • 作者简介:金立生（1975-），男，教授，博士生导师.研究方向：汽车安全，智能车辆导航.E-mail：jinls@ysu.edu.cn
  • 基金资助:
    国家自然科学基金项目(52072333);河北省重点研发计划项目(21340801D)

Dynamic multiple object detection algorithm for vehicle forward based on improved YOLOv3

Li-sheng JIN1,2(),Bai-cang GUO1,Fang-rong WANG3,Jian SHI4()   

  1. 1.School of Vehicle and Energy，Yanshan University，Qinhuangdao 066004，China
    2.Hebei Key Laboratory of Special Delivery Equipment，Yanshan University，Qinhuangdao 066004，China
    3.College of Communication Engineering，Jilin University，Changchun 130022，China
    4.School of Mechanical Engineering，Beijing Institute of Technology，Beijing 100081，China
  • Received:2020-07-31 Online:2021-07-01 Published:2021-07-14
  • Contact: Jian SHI E-mail:jinls@ysu.edu.cn;sj920443563@126.com

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摘要：

现阶段的环境感知目标检测技术多为单类目标检测，或是将一幅图像中所有目标均列为待检目标，较少有对处于车辆前方的目标进行针对性的划分和检测。为了解决以上问题，提出了将车辆前方的待检目标分为两类：一是危险性较大，随时可能发生位移的动态目标，包括四轮车辆、二轮车辆和人；二是危险性较小，不会发生位移的静态目标，包括交通信号灯和交通标识。针对危险性较大的车辆前方动态多目标，提出了一种可以移植于嵌入式端的改进YOLOv3的目标检测算法，针对原始YOLOv3算法得到模型较大，难以在嵌入式端实时检测的缺点，以轻量型骨干网络MobileNetV2替换YOLOv3原始骨干网络Darknet-53进行特征提取，在训练中加入群组归一化操作，并使用Adam作为优化器。使用提取后的BDD100K数据集进行训练，利用未参与训练的BDD100K部分数据集和自采标注的Team_test数据集进行测试。研究结果表明，相比于原始YOLOv3算法，本文算法的漏检率可以维持在5%以内，在mAP提升0.020的基础上，本文模型在参数量上较YOLOv3基础模型减小了约89%，在CPU下的Inference Time缩小了约70%。

关键词: 无人驾驶技术, 环境感知, 深度学习, 多目标检测, 轻量化模型

Abstract:

The task of object detection plays an important role in the safe driving of driverless vehicles. Currently, the object detection technology of environment percept is mostly one-class object detection or all the objects in an image are listed as the target to be detected. Numerous studies have not yet focused on object division and detection of the objects in front of the vehicle. To solve the above problems, in this paper, the objects to be detected in front of vehicles are divided into two categories. One is the dynamic targets with high risk and displacement at any time, including four-wheel vehicle, two-wheel vehicle and people. The other one is the static targets with less danger and no displacement, including traffic lights and traffic signs. For the dynamic multiple objects in front of the vehicle, an improved algorithm of object detection based on YOLOv3 is proposed, which can be transplanted to the embedded system. To overcome the shortcoming of the original YOLOv3 algorithm, that it is difficult to get real-time detection in the embedded terminal, the original backbone network Darknet-53 was replaced with MobileNetV2 to extract features, adding Group Normalization operation in the training process and using Adam as optimizer. The extracted BDD100K dataset is used for training. The model is tested with BDD100k partial dataset not involved in training and Team_test dataset produced by our research group. The results show that compared with original YOLOv3, the missing rate (MR) of the algorithm in this paper can be kept within 5%, and based on the increase of 0.020 in mAP, comparing with the basic model of YOLOv3, the parameters of YOLOv3-MobileNetV2 model are reduced by about 89%, the Inference Time is reduced by about 70% under the CPU.

Key words: driverless technology, environment percept, deep learning, multiple object detection, lightweight model

中图分类号: 

  • U491.2

图1

YOLOv3网络结构示意"

图2

Darknet-53网络"

图3

标准卷积核分解"

图4

MobileNets与MobileNetV2的微结构对比"

图5

ResNet与MobileNetV2的微结构对比"

图6

MobileNetV2网络"

图7

BN与GN示意图"

表1

实验采用的数据集"

Instance ObjectBDD100KTeam_test
Car1 021 84710 716
Bus16 5051 078
Truck42 9631 082
Bike10 229894
Motor4 296673
Person129 2626 023
Rider6 461834

图8

Loss变化曲线"

表2

两模型MR与EFR对比"

Index ModelMR/%EFR/%
YOLOv37.34.6
本文模型4.93.7

图9

混淆矩阵"

表3

两模型AP与mAP对比"

Model APYOLOv3本文模型
Car0.8330.864
Bus0.8190.827
Truck0.7840.775
Bike0.7430.763
Motor0.7160.729
Person0.8050.836
Rider0.7530.783
mAP0.7800.800

表4

两模型Params与Inference Time对比 (of two models)"

Model IndexYOLOv3本文模型
Params/MB246.528.3
Inference Time（CPU）/ms25681

图10

测试集检测效果可视化"

图11

实际场景检测效果可视化"

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