Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2898-2905.doi: 10.13229/j.cnki.jdxbgxb20220017

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Detection algorithm for parking space status based on convolution network structural re⁃parameterization

Xuan-jing SHEN1(),Tong-zhuang LIU1,Yu WANG1,Jia-wei LIU2()   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China
    2.Department of Human Resources,China Faw Group Import & Export Co. ,Ltd. ,Changchun 130119,China
  • Received:2022-01-04 Online:2022-12-01 Published:2022-12-08
  • Contact: Jia-wei LIU E-mail:xjshen@jlu.edu.cn;liujiawei17@faw.com.cn

Abstract:

In order to solve the problems of slow speed and low accuracy in parking space status detection algorithm, a detection algorithm for parking space status based on convolution network structural re-parameterization was proposed. The algorithm uses structural re-parameterization to decouple the training network and inference network. At training time, a multi-branch structure was formed using small convolutional kernels of different scales for simultaneously acquiring local detail features in parking space images, so that the network achieves high detection accuracy. After training, the training-time multi-branch structure was equivalently transformed into a single-branch structure for inference using structural re-parameterization, which significantly improves detection speed without loss of detection accuracy. In this way, the network can have both higher detection accuracy and faster inference speed during inference. The experimental results show that the proposed algorithm has obvious advantages in prediction accuracy and algorithm inference speed compared with other car position state detection algorithms.

Key words: computer application, parking space status detection, convolutional neural network, structure re-parameterization

CLC Number: 

  • TP391

Fig.1

Basic block"

Fig.2

Structure Re-parameterization"

Fig.3

Network structure for training"

Fig.4

Network structure for inference"

Fig.5

Parking lot pictures from PKLot dataset"

Fig.6

Parking lot pictures from CNRPark dataset"

Table 1

Comparison of results for different choices of the largest size convolution in the basic block"

模型训练精度/%测试精度/%推理时间/s参数量/个
PUCPRUFPR05
199.3896.7095.890.004139 234
299.3798.3597.330.005386 530
399.6898.4897.140.007757 474
499.6098.8097.010.0251 252 066

Table 2

Comparison of the results of different use numbers of basic blocks"

基本块

个数

训练精度/%测试精度/%
PUCPRUFPR05
499.3397.2795.31
599.3498.0997.04
699.3798.3597.33
799.5098.2595.66

Table 3

Comparisons of test accuracies for subsets of PKLot dataset"

网络名称

训练

子集

测试集精度/%
PUCPRUFPR04UFPR05
AlexNetPUCPR-88.8083.40
UFPR0489.50-87.60
UFPR0588.2087.30-
VGG16PUCPR-94.2090.80
UFPR0489.70-90.00
UFPR0590.5094.90-
VGG19PUCPR-93.8094.60
UFPR0480.40-91.90
UFPR0588.8095.10-
XceptionPUCPR-92.5093.30
UFPR0494.00-93.40
UFPR0595.7090.90-
Inception V3PUCPR-91.1094.20
UFPR0491.70-92.40
UFPR0594.3092.90-
ResNet34PUCPR-97.9596.63
UFPR0497.52-94.22
UFPR0595.5094.56-
本文PUCPR-98.3297.71
UFPR0498.35-97.33
UFPR0596.4395.81-

Table 4

Comparison of the accuracy of ResNet34 and ours on CNRPark dataset"

网络训练精度/%测试精度/%
ResNet3495.3697.79
本文95.9998.14

Table 5

Comparison of inference time and parameter amount between ResNet34 and ours"

网络推理时间/s参数量/个
ResNet340.10021 285 698
本文0.005386 530

Table 6

Comparison of the accuracy of mAlexNet and the proposed network on the PKLot dataset"

网络名称训练集测试集精度/%平均精度/%
mAlexNet

UFPR04

UFPR04

UFPR05

UFPR05

PUCPR

PUCPR

UFPR05

PUCPR

UFPR04

PUCPR

UFPR04

UFPR05

93.29

98.27

93.69

92.72

98.03

96.00

95.33
CarNet

UFPR04

UFPR04

UFPR05

UFPR05

PUCPR

PUCPR

UFPR05

PUCPR

UFPR04

PUCPR

UFPR04

UFPR05

97.60

98.30

95.20

98.40

94.40

97.60

96.92
本文

UFPR04

UFPR04

UFPR05

UFPR05

PUCPR

PUCPR

UFPR05

PUCPR

UFPR04

PUCPR

UFPR04

UFPR05

97.33

98.35

95.81

96.43

98.32

97.71

97.32

Table 7

Comparison of inference time and parameter amount between mAlexNet and ours"

网络推理时间/s参数量/个
mAlexNet0.00332 380
本文0.003386 530

Fig.7

Comparison of detection accuracy of mAlexNet and ours under occlusion dataset"

Fig.8

Detection results of the real parking"

Table 8

Comparison of detection times for parking lots with different number of parking spaces"

停车位数量/个时间/s
100.003
200.004

30

40

0.005

0.007

1 Ichihashi H, Notsu A, Honda K, et al. Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier[C]∥2009 IEEE International Conference on Fuzzy Systems, Jeju, Korea, 2009: 127-134.
2 Tsai L W, Hsieh J W, Fan K C. Vehicle detection using normalized color and edge map[J]. IEEE transactions on Image Processing, 2007, 16(3): 850-864.
3 Huang C C, Tai Y S, Wang S J. Vacant parking space detection based on plane-based Bayesian hierarchical framework[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(9): 1598-1610.
4 de Almeida P R L, Oliveira L S, Britto Jr A S, et al. PKLot——a robust dataset for parking lot classification[J]. Expert Systems with Applications, 2015, 42(11): 4937-4949.
5 刘日, 李建国, 王小农. 立体车库车位分配建模与仿真[J]. 江苏大学学报:自然科学版, 2018, 39(1):19-25.
Liu Ri, Li Jian-guo, Wang Xiao-nong. Modeling and simulation of parking space allocation in stereo garage[J]. Journal of Jiangsu University(Natural Science Edition), 2018, 39(1): 19-25.
6 于谦, 肖雄, 杨鸣鹏, 等. 基于车载排放测试驾驶行为对轻型汽油车排放的影响[J]. 江苏大学学报:自然科学版, 2022, 43(3):270-276.
Yu Qian, Xiao Xiong, Yang Ming-peng,et al. Driving behavior impact on emissions of light-duty gasoline vehicle based on portable emission measurement system[J]. Journal of Jiangsu University(Natural Science Edition), 2022, 43(3):270-276.
7 LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
8 申铉京, 沈哲, 黄永平, 等. 基于非局部操作的深度卷积神经网络车位占用检测算法[J]. 电子与信息学报, 2020, 42(9): 2269-2276.
Shen Xuan-jing, Shen Zhe, Huang Yong-ping, et al. Deep convolutional neural network for parking space occupancy detection based on non-local operation[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2269-2276.
9 Acharya D, Yan W, Khoshelham K. Real-time image-based parking occupancy detection using deep learning[C]∥Proceedings of the 5th Annual Research@Locate Conference, Adelaide, Australia, 2018: 33-40.
10 Amato G, Carrara F, Falchi F, et al. Deep learning for decentralized parking lot occupancy detection[J]. Expert Systems with Applications, 2017, 72: 327-334.
11 Amato G, Carrara F, Falchi F, et al. Car parking occupancy detection using smart camera networks and deep learning[C]∥2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 2016: 1212-1217.
12 Ding Xiao-han, Zhang Xing-yu, Ma Ning-ning, et al. Repvgg: making VGG-style convnets great again[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13733-13742.
13 蔡英凤, 王海, 陈龙, 江浩斌. 采用视觉显著性和深度卷积网络的鲁棒视觉车辆识别算法[J]. 江苏大学学报:自然科学版, 2015, 36(3): 331-336.
Cai Ying-feng, Wang hai, Chen Long, Jiang Hao-bin. Robust vehicle recognition algorithm using visual saliency and deep convolutional neural networks[J]. Journal of Jiangsu University(Natural Science Edition), 2015, 36(3): 331-336.
14 Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥International Conference on Machine Learning, Lille, France, 2015: 448-456.
15 Ding Xiao-han, Guo Yu-chen, Ding Gui-gang, et al. Acnet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 1911-1920.
16 Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
17 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]∥International Conference on Learning Representations, San Diego, CA, USA, 2015:1-14.
18 He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
19 Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 2818-2826.
20 Nurullayev S, Lee S W. Generalized parking occupancy analysis based on dilated convolutional neural network[J]. Sensors, 2019, 19(2): E277.
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