New website getting online, testing
    • 摘要: 在车辆识别和车辆年检时,正确识别车架上金属刻印的车辆识别代号(VIN)是非常重要的环节。针对VIN序列,本文提出了一种基于神经网络的旋转VIN图片识别方法,它由VIN检测和VIN识别两部分组成。首先,在EAST算法基础上利用轻量级神经网络提取特征,并结合文本分割实现快速、准确的VIN检测;其次,将VIN识别任务作为一个序列分类问题,提出了一种新的识别VIN方法,即通过位置相关的序列分类器,预测出最终的车辆识别代号。为了验证本文的识别方法,引入了一个VIN数据集,其中包含用于检测的原始旋转VIN图像和用于识别的水平VIN图像。实验结果表明,本文方法能有效地识别车架VIN图片,同时达到了实时性。

       

      Abstract: It is far essential to properly recognize the vehicle identification number (VIN) engraved on the car frame for vehicle surveillance and identification. In this paper, we propose an algorithm for recognizing rotational VIN images based on neural network which incorporates two components: VIN detection and VIN recognition. Firstly, with lightweight neural network and text segmentation based on EAST, we attain efficient and excellent VIN detection performance. Secondly, the VIN recognition is regarded as a sequence classification problem. By means of connecting sequential classifiers, we predict VIN characters directly and precisely. For validating our algorithm, we collect a VIN dataset, which contains raw rotational VIN images and horizontal VIN images. Experimental results show that the algorithm we proposed achieves good performance on VIN detection and VIN recognition in real time.