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Robust Detection and Recognition of Japanese Traffic Sign in the Complex Scenes Based on Deep Learning

机译:基于深度学习的复杂场景中的鲁棒检测与识别日本交通标志

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This study applies the object detection and recognition method using deep learning, which has been attracting considerable attention recently, for the detection and recognition of traffic signs. Furthermore, the advantages and disadvantages of the proposed method are discussed. During the detection and recognition of traffic signs, the following problems were identified. (1) The detection and recognition accuracy of a small object in an image, such as a distant road sign, was low. (2) The accuracy of the traffic sign detection and recognition was affected due to the changes in contrast at night and bad weather. Herein, we address these problems by learning the traffic signs through deep learning, which is robust against scale changes. Herein, we construct a Japanese traffic sign database and compared the methods used. Results demonstrate that the proposed approach demonstrates excellent detection and recognition accuracy.
机译:本研究适用于使用深度学习的对象检测和识别方法,这在最近一直吸引了相当大的关注,用于检测和识别交通标志。此外,讨论了该方法的优点和缺点。在检测和识别交通标志期间,确定了以下问题。 (1)图像中的小物体的检测和识别准确性,例如遥远的道路标志,低。 (2)由于夜间和恶劣天气的变化,交通标志检测和识别的准确性受到影响。在此,我们通过深入学习来学习交通标志来解决这些问题,这是对缩放变化的强大。在此,我们构建了日本的交通标志数据库,并比较了使用的方法。结果表明,该方法展示了出色的检测和识别准确性。

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