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Automatic Vehicle License Recognition Based on Video Vehicular Detection System

机译:基于视频车辆检测系统的自动驾驶执照识别

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摘要

Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90 percent to 94 percent under preferable illumination, while under poor condition, it increases more than 5 percent. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination.
机译:传统的车牌字符提取方法不能满足视频车辆检测系统对识别精度和速度的要求。因此,提出了一种基于多尺度边缘检测的车牌定位方法和一种基于马尔可夫随机场模型的字符分割算法。实验结果表明,与传统的基于差分算子边缘检测和基于阈值的字符分割的定位方法相比,该方法提取的许可证字符更加准确。在较好的照明条件下,精度从90%提高到94%,而在恶劣条件下,精度提高了5%以上。当使用两种改进的算法时,即使在嘈杂的环境或照明不均匀的情况下,自动许可证识别的准确性和速度也可以满足系统的要求。

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