Category-consistent deep network learning for accurate vehicle logo recognition

Lu, Wanglong; Zhao, Hanli; He, Qi; Huang, Hui; Jin, Xiaogang

doi:10.1016/j.neucom.2021.08.030

Cited by 10 publications

(5 citation statements)

References 29 publications

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“…However, shallow visual features are susceptible to variations in imaging conditions and thus have limited robustness and generalization capabilities. 13 Deep NNs have demonstrated remarkable performance on a wide range of visual tasks, such as object detection, 14,15 object tracking, 16,17 segmentation, 18,19 and image classification. 13,[20][21][22] Deep convolutional neural networks (CNN) [23][24][25][26] and transformers [27][28][29][30][31] are two good examples.…”

Section: Image Recognition Techniquesmentioning

confidence: 99%

“…13 Deep NNs have demonstrated remarkable performance on a wide range of visual tasks, such as object detection, 14,15 object tracking, 16,17 segmentation, 18,19 and image classification. 13,[20][21][22] Deep convolutional neural networks (CNN) [23][24][25][26] and transformers [27][28][29][30][31] are two good examples. To recognize images accurately, researchers have proposed various architectures and techniques for CNNs, such as using multiple layers, 23 skip connections, 24 dense connections, 25 squeeze and excitation steps, 32 attention mechanisms, 33 and large kernel attention.…”

Section: Image Recognition Techniquesmentioning

confidence: 99%

“…Traditional shallow machine learning algorithms, such as support vector machines and NN, using handcrafted low-level or shallow visual features 8 – 12 have been extensively studied extensively for image recognition tasks. However, shallow visual features are susceptible to variations in imaging conditions and thus have limited robustness and generalization capabilities 13 …”

Section: Related Workmentioning

confidence: 99%

“…Deep NNs have demonstrated remarkable performance on a wide range of visual tasks, such as object detection, 14 , 15 object tracking, 16 , 17 segmentation, 18 , 19 and image classification 13 , 20 – 22 Deep convolutional neural networks (CNN) 23 – 26 and transformers 27 – 31 are two good examples.…”

Section: Related Workmentioning

confidence: 99%

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TEG: image theme recognition using text-embedding-guided few-shot adaptation

Wang,

Lu,

Wang

et al. 2024

J. Electron. Imag.

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Grouping images into different themes is a challenging task in photo book curation. Unlike image object recognition, image theme recognition focuses on the understanding of the main subject or overall meaning conveyed by an image. However, it is challenging to achieve satisfactory performance using existing general image recognition methods. In this work, we aim to solve the image theme recognition task with few-shot training samples using pre-trained contrastive language-image models. A text-prompt-guided few-shot image adaptation framework is proposed, which incorporates a text-embedding-guided classifier and an auxiliary classification loss to exploit embedded visual and text features, stabilize the network training, and enhance recognition performance. We also present an annotated dataset Theme25 for studying image theme recognition. We conducted experiments on our Theme25 dataset as well as the publicly available CIFAR100 and ImageNet datasets to demonstrate the superiority of our method over the compared stateof-the-art methods.

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Section: Image Recognition Techniquesmentioning

confidence: 99%

Section: Image Recognition Techniquesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

TEG: image theme recognition using text-embedding-guided few-shot adaptation

Wang,

Lu,

Wang

et al. 2024

J. Electron. Imag.

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“…Peng et al 17 proposed a detection method based on statistical random sparse distribution that considered the correlation between images and described the distribution of a grayscale image. Lu et al 18 designed a CNN model that considered both high-level and low-level features to extract deep features and used a novel category-consistent mask learning module to help the framework detect logos.…”

Section: Vehicle Logo Detectionmentioning

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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