This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.
翻译:本文研究车辆制造和模型分类的问题。 其中一些主要挑战正在达到高分类精度并缩短图像的批注时间。 为了解决这些问题,我们利用土耳其的在线车辆市场创建了一个精细的分类数据库。 提议建立一个管道,将SSD(Singshot多箱探测器)模型与CNN(Convolutional Neal Network)模型结合起来,在数据库上进行培训。 在管道中,我们首先采用减少批注时间的算法来检测车辆。 然后,我们把它们输入CNN模型,比使用常规CNN模型的分类精度高约4%。 下一步,我们提议将所探测到的车辆用作图像的地面真相捆绑箱(GTBB),并将其输入另一个管道的SSD模型中。 在现阶段,在没有完全使用 GTBBB 的情况下,它达到了合理的分类精度结果。 最后,我们通过使用我们提议的管道在使用的案件中应用了一种应用程序。 它通过比较其牌照编号和制作模型来检测未经许可的车辆。 它假定, 牌照牌号是可以读的。