Multiple-license plate recognition is gaining popularity in the Intelligent Transport System (ITS) applications for security monitoring and surveillance. Advancements in acquisition devices have increased the availability of high definition (HD) images, which can capture images of multiple vehicles. Since license plate (LP) occupies a relatively small portion of an image, therefore, detection of LP in an image is considered a challenging task. Moreover, the overall performance deteriorates when the aforementioned factor combines with varying illumination conditions, such as night, dusk, and rainy. As it is difficult to locate a small object in an entire image, this paper proposes a two-step approach for plate localization in challenging conditions. In the first step, the Faster-Region-based Convolutional Neural Network algorithm (Faster R-CNN) is used to detect all the vehicles in an image, which results in scaled information to locate plates. In the second step, morphological operations are employed to reduce non-plate regions. Meanwhile, geometric properties are used to localize plates in the HSI color space. This approach increases accuracy and reduces processing time. For character recognition, the look-up table (LUT) classifier using adaptive boosting with modified census transform (MCT) as a feature extractor is used. Both proposed plate detection and character recognition methods have significantly outperformed conventional approaches in terms of precision and recall for multiple plate recognition.

多车牌识别在用于安全监控的智能交通系统 (ITS) 应用中越来越受欢迎。采集设备的进步提高了高清 (HD) 图像的可用性，可以捕获多辆车的图像。由于车牌 (LP) 占据图像的相对较小的部分，因此，图像中的 LP 检测被认为是一项具有挑战性的任务。此外，当上述因素与变化的照明条件（例如夜间、黄昏和雨天）相结合时，整体性能会下降。由于很难在整个图像中定位小物体，本文提出了一种在具有挑战性的条件下进行板定位的两步方法。在第一步中，基于更快区域的卷积神经网络算法 (Faster R-CNN) 用于检测图像中的所有车辆，从而产生用于定位车牌的缩放信息。第二步，采用形态学操作来减少非板块区域。同时，几何属性用于在 HSI 颜色空间中定位板。这种方法提高了准确性并减少了处理时间。对于字符识别，使用使用自适应提升和修正的人口普查变换 (MCT) 作为特征提取器的查找表 (LUT) 分类器。提出的车牌检测和字符识别方法在多车牌识别的精度和召回率方面都明显优于传统方法。第二步，采用形态学操作来减少非板块区域。同时，几何属性用于在 HSI 颜色空间中定位板。这种方法提高了准确性并减少了处理时间。对于字符识别，使用使用自适应提升和修正的人口普查变换 (MCT) 作为特征提取器的查找表 (LUT) 分类器。提出的车牌检测和字符识别方法在多车牌识别的精度和召回率方面都明显优于传统方法。第二步，采用形态学操作来减少非板块区域。同时，几何属性用于在 HSI 颜色空间中定位板。这种方法提高了准确性并减少了处理时间。对于字符识别，使用使用自适应提升和修正的人口普查变换 (MCT) 作为特征提取器的查找表 (LUT) 分类器。提出的车牌检测和字符识别方法在多车牌识别的精度和召回率方面都明显优于传统方法。查找表 (LUT) 分类器使用自适应提升和修正的人口普查变换 (MCT) 作为特征提取器。提出的车牌检测和字符识别方法在多车牌识别的精度和召回率方面都明显优于传统方法。查找表 (LUT) 分类器使用自适应提升和修正的人口普查变换 (MCT) 作为特征提取器。提出的车牌检测和字符识别方法在多车牌识别的精度和召回率方面都明显优于传统方法。