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Quality of dirty: A decision making assessment methodology for automatic license plate recognition under dirtied conditions

机译：脏污质量：在脏污情况下自动识别车牌的决策评估方法

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Automatic license plate recognition (ALPR) from vehicle images plays an important role in intelligent traffic management. A critical problem is that the cameras can be dirtied such that the captured images are unrecognizable. This paper defines a new concept of Quality of Dirty (QoD) of captured images indicating when the dirtied cameras need to be cleaned. An accurate image QoD metric is proposed based on relevant image features and support vector regression (SVR), and it can handle a tricky issue of differentiating captured imaged containing dirtied vehicles from images captured by a dirtied camera. A subjective assessment to collect ground-truth QoD scores of real captured images has also been conducted. Experiments have demonstrated that the proposed image QoD metric achieves high accuracy when predicting the dirtied degree of cameras in the tunnel environment.
机译：车辆图像的自动车牌识别（ALPR）在智能交通管理中起着重要作用。一个关键问题是相机可能被弄脏，以至于无法识别所捕获的图像。本文定义了捕获图像的脏质量（QoD）的新概念，该概念指示何时需要清洁脏污的相机。基于相关的图像特征和支持向量回归（SVR），提出了一种精确的图像QoD度量标准，它可以解决一个棘手的问题，即将包含脏车的捕获图像与脏相机捕获的图像区分开。还进行了主观评估，以收集真实捕获图像的真实QoD分数。实验表明，在预测隧道环境中摄像机的脏污程度时，提出的图像QoD度量可以达到较高的精度。

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