Vehicle Logo Recognition Based on Enhanced Matching for Small Objects, Constrained Region and SSFPD Network

Liu, Ruikang; Han, Qing; Min, Weidong; Zhou, Linghua; Xu, Jianqiang

doi:10.3390/s19204528

Cited by 13 publications

(7 citation statements)

References 45 publications

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“…Then, vehicle logo classification was performed with deep belief networks (DBNs). Liu et al [20] proposed a single shot feature pyramid detector (SSFPD) based on a reduced ResNetXt model for small-sized vehicle logo detection and classification. Yuxin and H. Peifeng [21] designed a highway entrance VLR system based on VGGNet, in which some simplified VGGNet and fine-tuned VGG16 were used to complete the work.…”

Section: B Vlr Methods Based On Deep Learningmentioning

confidence: 99%

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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In this paper, we present a novel learning-based scheme for vehicle logo recognition (VLR). This scheme is termed Multilayer Pyramid Network Based on Learning (MLPNL) and is based on the principle that considering multiple resolutions is helpful for extracting valuable features that benefit the final recognition performance. The innovations of this scheme include (1) a multilayer pyramid network, with pixel difference matrices (PDMs) as its input and output and feature parameters mapping one PDM to another; (2) an objective function and a corresponding optimization method designed to facilitate the learning of the feature parameters of the proposed multilayer pyramid network; and (3) a multi-codebook-based encoding method that makes best use of the features extracted from PDMs corresponding to different resolutions. Extensive experiments conducted with an open dataset, HFUT-VL, demonstrate that the proposed MLPNL scheme outperforms state-of-the-art handcrafted descriptors and non-deep-learning-based learning methods when fewer training samples exist. Experiments conducted with a benchmark dataset, XMU, demonstrate that MLPNL outperforms existing state-ofthe-art VLR methods. Experiments conducted both on HFUT-VL and XMU demonstrate that MLPNL is faster than most deep-learning-based learning methods while maintaining nearly the same recognition rate. Code has been made available at: https://github.com/HFUT-CV/MLPNL.

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Section: B Vlr Methods Based On Deep Learningmentioning

confidence: 99%

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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“…Despite the vehicle logo recognition problem has been studied for many years, a few publicly available is available for the computer vision community. There are a few datasets is applied for logo detection such as vehicle logo dataset with 30 classes, namely VLD-30 [38]. Table 1 analyses the existing vehicle logo datasets.…”

Section: Methodsmentioning

confidence: 99%

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

2020

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Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.

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“…The author asserted that it is the first standard dataset for vehicle logo detection. Liu et al 6 12 proposed a data optimization method based on the combination of multiple strategies.…”

Section: Datasetsmentioning

confidence: 99%

Expressway vehicle logo detection: a lightweight CNN and logo localization method

Yue,

Fu,

Yang

2024

J. Electron. Imag.

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Information about vehicles plays an important role in intelligent transportation systems (ITSs). It can be applied in different areas such as vehicle monitoring, vehicle detection, auxiliary reconnaissance, etc. Among the different types of vehicle-related information, logo information plays an essential role in quickly identifying the vehicle and enabling relevant work to be carried out. However, existing logo detection methods face issues related to low training accuracy and difficulty in accurately locating the logo, which leads to inaccurate detection of vehicle logos. To address these challenges, we first propose a method for detecting vehicle logos, particularly at expressway exits. We created a dataset comprising seven categories of vehicles for this purpose. Our solution includes a lightweight model that bridges CNN and transformer and a creative method for locating the logo. Additionally, we also do data processing on the test images to make them robust to environmental changes. The network that we designed is simple yet effective, achieving improvements in both precision and speed. Furthermore, our vehicle logo localization algorithm can withstand environmental variations. Experimental results demonstrate that our algorithm achieves a 5% to 10% accuracy boost compared with other methods.

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Vehicle Logo Recognition Based on Enhanced Matching for Small Objects, Constrained Region and SSFPD Network

Cited by 13 publications

References 45 publications

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

Expressway vehicle logo detection: a lightweight CNN and logo localization method

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