Bangladeshi Number Plate Detection: Cascade Learning vs. Deep Learning

M. Pias, Aunnoy K. Mutasim, M. Amin
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引用次数: 2

Abstract

This work investigated two different machine learning techniques: Cascade Learning and Deep Learning, to find out which algorithm performs better to detect the number plate of vehicles registered in Bangladesh. To do this, we created a dataset of about 1000 images collected from a security camera of Independent University, Bangladesh. Each image in the dataset were then labelled manually by selecting the Region of Interest (ROI). In the Cascade Learning approach, a sliding window technique was used to detect objects. Then a cascade classifier was employed to determine if the window contained object of interest or not. In the Deep Learning approach, CIFAR-10 dataset was used to pre-train a 15-layer Convolutional Neural Network (CNN). Using this pretrained CNN, a Regions with CNN (R-CNN) was then trained using our dataset. We found that the Deep Learning approach (maximum accuracy 99.60% using 566 training images) outperforms the detector constructed using Cascade classifiers (maximum accuracy 59.52% using 566 positive and 1022 negative training images) for 252 test images.
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孟加拉国车牌检测:级联学习与深度学习
这项工作调查了两种不同的机器学习技术:级联学习和深度学习,以找出哪种算法在检测孟加拉国注册的车辆号牌方面表现更好。为此,我们创建了一个数据集,其中包括从孟加拉国独立大学的安全摄像头收集的大约1000张图像。然后通过选择感兴趣区域(ROI)手动标记数据集中的每个图像。在级联学习方法中,使用滑动窗口技术来检测目标。然后使用级联分类器来确定窗口是否包含感兴趣的对象。在深度学习方法中,使用CIFAR-10数据集预训练一个15层卷积神经网络(CNN)。使用这个预训练的CNN,然后使用我们的数据集训练一个带有CNN的区域(R-CNN)。我们发现深度学习方法(使用566张训练图像的最大准确率为99.60%)在252张测试图像上优于使用级联分类器构建的检测器(使用566张正训练图像和1022张负训练图像的最大准确率为59.52%)。
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