Mirrored EAST: An Efficient Detector for Automatic Vehicle Identification Number Detection in the Wild

Vehicle identification number (VIN) is a unique serial number used to identify individual vehicles across various applications. The first crucial step in automatically collecting VINs is to accurately localize the VIN area. In this article, we present a novel VIN detection approach called Mirrored EAST (MEAST) based on an efficient and accurate scene text (EAST) Detection framework. MEAST learns to exploit the spatial consistency between an image and its mirrored version to improve localization performance, and employs a lighter but more discriminative backbone network to improve its applicability in mobile scenarios. To evaluate the VIN detection performance, we constructed a large-scale VIN image dataset named CQU-VD20 K, consisting of 20 000 VIN images in real scenarios. Based on this dataset, we have conducted a comprehensive empirical study of VIN detection. The results demonstrate the superiority of MEAST over other methods in VIN detection. Additionally, we also conducted extended experiments on a license plate dataset named CCPD-Rotate, which confirms the effectiveness of our approach in other industrial inspection tasks.