在无人机图像中采用Swin变压器进行车牌号码和文本检测

Srikanta Pal, Ayush Roy, Palaiahnakote Shivakumara, Umapada Pal
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引用次数: 1

摘要

无人机和无人驾驶飞行器在监控非法停车、追踪车辆、控制交通堵塞、追赶车辆等各种实际应用中的使用显著增加。然而,与大多数现有的专注于正常图像的文本/车牌号码检测方法不同,由于图像捕获过程中高度距离和倾斜角度的变化,无人机图像中车牌号码的准确检测变得复杂和具有挑战性。为了解决这一问题,本文提出了一种基于Swin变压器的无人机图像车牌号码检测新模型。Swin变压器的选择是由于其特殊的性能,如更高的精度,效率和更少的计算,使其适用于无人机图像中的车牌号码/文本检测。为了进一步提高所提模型在退化、质量差和遮挡等不利条件下的性能,所提的工作结合了基于最大稳定极值区域(MSER)的区域建议网络(RPN)来表示图像中的文本数据。在普通车牌和无人机图像上的实验结果表明,所提出的模型优于最先进的方法。
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Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images
The use of drones and unmanned aerial vehicles has significantly increased in various real-world applications such as monitoring illegal car parking, tracing vehicles, controlling traffic jams, and chasing vehicles. However, accurate detection of license plate numbers in drone images becomes complex and challenging due to variations in height distances and oblique angles during image capturing, unlike most existing methods that focus on normal images for text/license plate number detection. To address this issue, this work proposes a new model for License Plate Number Detection in Drone Images using Swin Transformer. The Swin Transformer is chosen due to its special properties such as higher accuracy, efficiency, and fewer computations, making it suitable for license plate number/text detection in drone images. To further improve the performance of the proposed model under adverse conditions such as degradations, poor quality, and occlusion, the proposed work incorporates a Maximally Stable Extremal Regions (MSER) based Regional Proposal Network (RPN) to represent text data in the images. Experimental results on both normal license plates and drone images demonstrate the superior performance of the proposed model over state-of-the-art methods.
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