Adapting a Swin Transformer for License Plate Number and Text Detection in Drone Images

Abstract

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.