论文标题
带有新型数据集的车辆和车牌识别收集数据集
Vehicle and License Plate Recognition with Novel Dataset for Toll Collection
论文作者
论文摘要
我们提出了一个自动收集框架，包括三个步骤：车辆类型识别，车牌定位和阅读。但是，由于几个因素引起的图像变化，这三个步骤中的每个步骤都变得不平凡。前部的传统车辆装饰会导致相同类型的车辆之间的变化。这些装饰使车牌定位和由于严重的背景混乱和部分遮挡而难以识别。同样，在大多数车辆，尤其是卡车上，车牌的位置不一致。最后，对于车牌读数，这些变化是由非均匀的字体样式，大小和部分遮挡的字母和数字引起的。我们提出的框架利用了骨干深学习体系结构的数据可用性和性能评估。我们收集了一个新颖的数据集，\ emph {多样的车辆和车牌数据集（DVLPD）}，由属于六种车辆类型的10K图像组成。然后，为车辆类型，车牌及其字符和数字手动注释每个图像。对于这三个任务中的每个任务，我们都会评估您仅查看一次（Yolo）V2，Yolov3，Yolov4和Fasterrcnn。为了在Raspberry Pi上实时实现，我们评估了名为Tiny Yolov3和Tiny Yolov4的Yolo的更轻版本。 Yolov4可实现最佳的平均平均平均精度（[email protected]）为98.8％，车牌检测98.5％，车牌读数为98.3％，而车牌读数为98.3％，而其较轻的Yolov4（即，Tiny Yolov4的较轻版本）获得了97.1％，97.4％和93.33.7％的牌照，并读取了93.4％的牌照和93.7％的识别，并识别了93.7％的牌照，并识别了93.7％的牌照，并获得了牌照，并获得了牌照，并获得了识别，并获得了牌照，并获得了93.4％的牌照，并获得了牌照。数据集和培训代码可在https://github.com/usama-x930/vt-lpr上找到
We propose an automatic framework for toll collection, consisting of three steps: vehicle type recognition, license plate localization, and reading. However, each of the three steps becomes non-trivial due to image variations caused by several factors. The traditional vehicle decorations on the front cause variations among vehicles of the same type. These decorations make license plate localization and recognition difficult due to severe background clutter and partial occlusions. Likewise, on most vehicles, specifically trucks, the position of the license plate is not consistent. Lastly, for license plate reading, the variations are induced by non-uniform font styles, sizes, and partially occluded letters and numbers. Our proposed framework takes advantage of both data availability and performance evaluation of the backbone deep learning architectures. We gather a novel dataset, \emph{Diverse Vehicle and License Plates Dataset (DVLPD)}, consisting of 10k images belonging to six vehicle types. Each image is then manually annotated for vehicle type, license plate, and its characters and digits. For each of the three tasks, we evaluate You Only Look Once (YOLO)v2, YOLOv3, YOLOv4, and FasterRCNN. For real-time implementation on a Raspberry Pi, we evaluate the lighter versions of YOLO named Tiny YOLOv3 and Tiny YOLOv4. The best Mean Average Precision ([email protected]) of 98.8% for vehicle type recognition, 98.5% for license plate detection, and 98.3% for license plate reading is achieved by YOLOv4, while its lighter version, i.e., Tiny YOLOv4 obtained a mAP of 97.1%, 97.4%, and 93.7% on vehicle type recognition, license plate detection, and license plate reading, respectively. The dataset and the training codes are available at https://github.com/usama-x930/VT-LPR
