In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization;2) character segmentation;and 3) feature extract...In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization;2) character segmentation;and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.展开更多
传统中文车牌识别方法对场景约束有要求,算法实时性差,且无法被部署在边缘设备上。针对上述问题,文中提出一种基于YOLO(You Only Look Once)的无约束场景中文车牌检测与识别方法。该方法分为车牌检测和车牌字符识别两个模块。在车牌检...传统中文车牌识别方法对场景约束有要求,算法实时性差,且无法被部署在边缘设备上。针对上述问题,文中提出一种基于YOLO(You Only Look Once)的无约束场景中文车牌检测与识别方法。该方法分为车牌检测和车牌字符识别两个模块。在车牌检测部分,使用改进的YOLOv5模型,在预测目标候选区域的基础上多预测4组关键点用于车牌矫正,并使用在COCO数据集上训练的预训练模型进行训练,减少了由环境复杂引起的误检问题,具有高实时性。在车牌字符识别部分,改进了CRNN(Convolutional Recurrent Neural Network)模型,减少了算法的参数量和计算量,使其能成功部署于各类边缘设备。实验结果表明所提出的车牌识别方法能在复杂环境中高效检测并识别车牌。文中提出的车牌检测模型在车牌检测数据集上的map值相较Retina-face提升了3.0%,车牌字符识别模型在车牌识别数据集上精确度相比LPR-Net提升了4.2%。展开更多
文摘In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization;2) character segmentation;and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.
