Improving Warped Planar Object Detection Network For Automatic License Plate Recognition

TL;DR

This paper enhances the WPOD-Net license plate recognition system by integrating edge detection via Sobel filters, significantly improving accuracy through feature engineering.

Contribution

The paper introduces a novel edge-aware feature engineering method using Sobel filters to improve WPOD-Net's license plate detection accuracy.

Findings

Significant performance improvement demonstrated by higher Quadrilateral IoU scores.

Edge information integration enhances license plate contour detection.

Proposed method outperforms the original WPOD-Net model.

Abstract

This paper aims to improve the Warping Planer Object Detection Network (WPOD-Net) using feature engineering to increase accuracy. What problems are solved using the Warping Object Detection Network using feature engineering? More specifically, we think that it makes sense to add knowledge about edges in the image to enhance the information for determining the license plate contour of the original WPOD-Net model. The Sobel filter has been selected experimentally and acts as a Convolutional Neural Network layer, the edge information is combined with the old information of the original network to create the final embedding vector. The proposed model was compared with the original model on a set of data that we collected for evaluation. The results are evaluated through the Quadrilateral Intersection over Union value and demonstrate that the model has a significant improvement in performance.