RDMMLND: A New Robust Deep Model for Multiple License Plate Number Detection in Video

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Conference Article

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


Accurate multiple license plate detection without affecting speed, occlusion, low contrast and resolution, uneven illumination effect and poor quality is an open challenge. This study presents a new Robust Deep Model for Multiple License Plate Number Detection (RDMMLND). To cope with the above-mentioned challenges, the proposed work explores YOLOv5 for detecting vehicles irrespective of type to reduce background complexity in the images. For detected vehicle regions, we propose a new combination of Wavelet Decomposition and Phase Congruency Model (WD-PCM), which enhances the license plate number region such that the license plate number detection step fixes correct bounding boxes for each vehicle of the input images. The proposed model is tested on our own dataset containing video images and standard dataset of license plate number detection to show that the proposed model is useful and effective for multiple license plate number detection. Furthermore, the proposed method is tested on natural scene text datasets to show that the proposed method can be extended to address the challenges of natural scene text detection.

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