Unsupervised Domain Adaptive Corner Detection in Vehicle Plate Images

Jun, Kyungkoo

doi:10.3390/s22176565

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Also, there are cases where blurring occurs in the resulting image, which leads to a decrease in recognition performance [9]. A domain adaptation technique was tried in [10], where a prediction model trained for the plates of one country is adapted to the same task but for the plates of a diferent country.…”

Section: Related Workmentioning

confidence: 99%

Is Vehicle Plate Corner Prediction by Vision Transformer Better than CNNs?

Jun

2023

Scientific Programming

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The license plate recognition performance can be improved by converting the license plate photographed from the side to the front view. To perform this transformation, four vertex corner positions of the license plate are required. Existing deep learning methods to find these corner positions use a convolutional neural network (CNN). In this study, we propose a model using a vision transformer (ViT), a nonconvolutional method that has recently been attracting attention, as a backbone, and compare its performance with existing CNN models. The ablation study is conducted by diversifying the ViT structure. Through these results, it was found that ViT has strengths in model size reduction but is similar in performance or inferior to CNN and that ViT training is more difficult than CNN.

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Section: Related Workmentioning

confidence: 99%

Is Vehicle Plate Corner Prediction by Vision Transformer Better than CNNs?

Jun

2023

Scientific Programming

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A Benchmark for the Evaluation of Corner Detectors

2022

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Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified metrics to evaluate their corner detection performance. In this paper, we build three benchmark datasets for corner detection. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies. The third one contains a set of urban images, called the Urban-Corner dataset. For each test image in these three datasets, the ground-truth corners are manually labeled as objectively as possible with the assistance of a line segment detector. Then, a set of benchmark evaluation metrics is suggested, including five conventional ones: the precision, the recall, the arithmetic mean of precision and recall (APR), the F score, the localization error (Le), and a new one proposed in this work called the repeatability referenced to ground truth (RGT). Finally, a comprehensive evaluation of current state-of-the-art corner detectors is conducted.

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Correction Method for Perspective Distortions of Pipeline Images

Zhang,

Zhou,

et al. 2024

Electronics

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It is common to find severe perspective distortion in a pipeline’s image in medium-diameter pipeline defect detection by the panoramic image unwrapping method, resulting in low-quality image unwrapping and stitching, which is caused by the camera’s optical axis being completely deviated from the pipeline’s center. To solve this problem, a novel correction method for reducing perspective distortion in pipeline images was proposed for pipeline defect detection. Firstly, the method enhances the edges of unevenly illuminated regions within a pipeline to facilitate image segmentation and identify key points necessary for correcting perspective distortion. Then, a six-feature-point extraction method was proposed for a circle target to establish the projection relationship between the extracted feature and mapped points on the reference circle. Finally, a perspective matrix was constructed to complete the perspective transformation correction of the distorted images. The results show that the average correction rate and the average relative error of the proposed correction method can reach 90.85% and 1.31%, respectively. The study innovatively used the enhancement of uneven illumination to find distorted edge information. It proposed an extraction method using a reference circle and six key feature points to build a mapping model. It can provide a novel method which can be used to obtain a superior image for pipeline detection and lay a solid foundation for subsequent high-quality pipeline image stitching.

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Unsupervised Domain Adaptive Corner Detection in Vehicle Plate Images

Cited by 3 publications

References 24 publications

Is Vehicle Plate Corner Prediction by Vision Transformer Better than CNNs?

Is Vehicle Plate Corner Prediction by Vision Transformer Better than CNNs?

A Benchmark for the Evaluation of Corner Detectors

Correction Method for Perspective Distortions of Pipeline Images

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