论文标题
改善自动车牌识别的翘曲平面对象检测网络
Improving Warped Planar Object Detection Network For Automatic License Plate Recognition
论文作者
论文摘要
本文旨在使用功能工程提高精度来改善扭曲的刨床对象检测网络（WPOD-NET）。使用功能工程使用翘曲对象检测网络解决了哪些问题？更具体地说，我们认为在图像中添加有关边缘的知识是有意义的，以增强确定原始WPOD-NET模型的车牌轮廓的信息。 SOBEL滤波器已通过实验选择并充当卷积神经网络层，边缘信息与原始网络的旧信息结合在一起，以创建最终的嵌入向量。将提出的模型与我们收集的一组数据的原始模型进行了比较。结果通过四边形交集对联合价值进行评估，并证明该模型的性能有显着改善。
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.
