VLDetNet: an improved vehicle logo detection method based on vBlock

Yu, Ye; Shi, Daizong; Zhu, Wen-Hong; Liang, Zhen; Li, Hua; Jia, Wei; Lü, Qiang

doi:10.1117/1.jei.31.6.063042

Cited by 2 publications

(2 citation statements)

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“…However, detecting vehicle logos at expressway exits presents unique challenges compared with other scenes. 4 On the one hand, the camera used to capture the image is fixed, resulting in similar angles and heights for all collected logos. On the other hand, the changes in the outdoor environment can influence the quality of images.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…Therefore, accurate detection of these logos has become increasingly necessary. However, detecting vehicle logos at expressway exits presents unique challenges compared with other scenes 4 . On the one hand, the camera used to capture the image is fixed, resulting in similar angles and heights for all collected logos.…”

Section: Introductionmentioning

confidence: 99%

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

Yue,

Fu,

Yang

2024

J. Electron. Imag.

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Deep Learning for Logo Detection: A Survey

Hou

Min

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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Logo detection has gradually become a research hotspot in the field of computer vision and multimedia for its various applications, such as social media monitoring, intelligent transportation and video advertising recommendation. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, and loss functions have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and their strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection. This study aims better to inform readers about the current state of logo detection and encourage more researchers to get involved in logo detection.

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VLDetNet: an improved vehicle logo detection method based on vBlock

Cited by 2 publications

References 40 publications

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

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

Deep Learning for Logo Detection: A Survey

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