基于卷积网络结构重参数化的车位状态检测算法

doi:10.13229/j.cnki.jdxbgxb20220017

Abstract

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

Xuan-jing SHEN,Tong-zhuang LIU,Yu WANG,Jia-wei LIU. Detection algorithm for parking space status based on convolution network structural re⁃parameterization[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2898-2905.

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模型	训练精度/%	测试精度/%		推理时间/s	参数量/个
模型	训练精度/%	PUCPR	UFPR05	推理时间/s	参数量/个
1	99.38	96.70	95.89	0.004	139 234
2	99.37	98.35	97.33	0.005	386 530
3	99.68	98.48	97.14	0.007	757 474
4	99.60	98.80	97.01	0.025	1 252 066

基本块个数	训练精度/%	测试精度/%
基本块个数	训练精度/%	PUCPR	UFPR05
4	99.33	97.27	95.31
5	99.34	98.09	97.04
6	99.37	98.35	97.33
7	99.50	98.25	95.66

网络名称	训练子集	测试集精度/%
网络名称	训练子集	PUCPR	UFPR04	UFPR05
AlexNet	PUCPR	-	88.80	83.40
	UFPR04	89.50	-	87.60
	UFPR05	88.20	87.30	-
VGG16	PUCPR	-	94.20	90.80
	UFPR04	89.70	-	90.00
	UFPR05	90.50	94.90	-
VGG19	PUCPR	-	93.80	94.60
	UFPR04	80.40	-	91.90
	UFPR05	88.80	95.10	-
Xception	PUCPR	-	92.50	93.30
	UFPR04	94.00	-	93.40
	UFPR05	95.70	90.90	-
Inception V3	PUCPR	-	91.10	94.20
	UFPR04	91.70	-	92.40
	UFPR05	94.30	92.90	-
ResNet34	PUCPR	-	97.95	96.63
	UFPR04	97.52	-	94.22
	UFPR05	95.50	94.56	-
本文	PUCPR	-	98.32	97.71
	UFPR04	98.35	-	97.33
	UFPR05	96.43	95.81	-

网络	训练精度/%	测试精度/%
ResNet34	95.36	97.79
本文	95.99	98.14

网络	推理时间/s	参数量/个
ResNet34	0.100	21 285 698
本文	0.005	386 530

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

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