A weakly supervised learning method for vehicle identification code detection and recognition

Cao Zhi; Shang Lidan; Yin Dong

doi:10.12086/oee.2021.200270

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Feb 2021

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JOURNAL OF MECHANICAL ENGINEERING > 2021 > 48(2): 200270.

Cao Zhi, Shang Lidan, Yin Dong. A weakly supervised learning method for vehicle identification code detection and recognition[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 48(2): 200270. doi: 10.12086/oee.2021.200270

Citation:

Cao Zhi, Shang Lidan, Yin Dong. A weakly supervised learning method for vehicle identification code detection and recognition[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 48(2): 200270. doi: 10.12086/oee.2021.200270

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A weakly supervised learning method for vehicle identification code detection and recognition

doi: 10.12086/oee.2021.200270

Cao Zhi^{1, 2
,},
Shang Lidan^{1, 2},
Yin Dong^{1, 2
,
,}

Funds:

Key Research and Development Plan Projects in Anhui Province 1804a09020049

More Information

Corresponding author: Yin Dong, E-mail: yindong@ustc.edu.cn
Received Date: 18 Jul 2020
Rev Recd Date: 23 Oct 2020

Abstract

Abstract

The vehicle identification code (VIN) is of great significance to the annual vehicle inspection. However, due to the lack of character-level annotations, it is impossible to perform the single-character style check on the VIN. To solve this problem, a single-character detection and recognition framework for VIN is designed and a weakly supervised learning algorithm without character-level annotation is proposed for this framework. Firstly, the feature information of each level of VGG16-BN is fused to obtain a fusion feature map with single-character position information and semantic information. Secondly, a network structure for both the character detection branch and the character recognition branch is designed to extract the position and semantic information of a single character in the fusion feature map. Finally, using the text length and single-character category information, the proposed framework is weakly supervised on the vehicle identification code data set without character-level annotations. On the VIN test set, experimental results show that the proposed method realizes the Hmean score of 0.964 and a single-character detection and recognition accuracy rate of 95.7%, showing high practicability.
- convolutional neural network,
- weakly supervised learning,
- scene text detection,
- scene text recognition,
- vehicle identification number (VIN)

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A weakly supervised learning method for vehicle identification code detection and recognition

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