Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1427-1436.doi: 10.13229/j.cnki.jdxbgxb20200588

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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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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

CLC Number: 

  • U491.2

Fig.1

Network structure of YOLOv3"

Fig.2

Network of Darknet-53"

Fig.3

Standard convolutional filter decomposition"

Fig.4

Microstructure comparison between MobileNets and MobileNetV2"

Fig.5

Microstructure comparison between ResNet and MobileNetV2"

Fig.6

Network of MobileNetV2"

Fig.7

Diagrams of BN and GN"

Table 1

Data sets for testing"

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

Fig.8

Loss change curve"

Table 2

Comparison for MR and EFR of two models"

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

Fig.9

Confusion Matrix"

Table 3

Comparison for AP and mAP of two models"

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

Table 4

Comparison for Params and Inference Time"

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

Fig.10

Visualization of detection on test set"

Fig.11

Visualization of detection on real-world"

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