Vehicle Logo Recognition with Small Sample Problem in Complex Scene Based on Data Augmentation

Xiao Ke and Pengqiang Du

Mathematical Problems in Engineering, 2020, vol. 2020, 1-10

Abstract:

Automatic identification for vehicles is an important topic in the field of Intelligent Transportation Systems (ITS), and the vehicle logo is one of the most important characteristics of a vehicle. Therefore, vehicle logo detection and recognition are important research topics. Because of the problems that the area of a vehicle logo is too small to be detected and the dataset is too small to train for complex scenes, considering the speed of recognition and the robustness to complex scenes, we use deep learning methods which are based on data optimization for vehicle logo in complex scenes. We propose three augmentation strategies for vehicle logo data: cross-sliding segmentation method, small frame method, and Gaussian Distribution Segmentation method. For the problem of small sample size, we use cross-sliding segmentation method, which can effectively increase the amount of data without changing the aspect ratio of the original vehicle logo image. To expand the area of the logos in the images, we develop the small frame method which improves the detection results of the small area vehicle logos. In order to enrich the position diversity of vehicle logo in the image, we propose Gaussian Distribution Segmentation method, and the result shows that this method is very effective. The F 1 value of our method in the YOLO framework is 0.7765, and the precision is greatly improved to 0.9295. In the Faster R-CNN framework, the F 1 value of our method is 0.7799, which is also better than before. The results of experiments show that the above optimization methods can better represent the features of the vehicle logos than the traditional method, and the experimental results have been improved.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/6591873.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/6591873.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6591873

DOI: 10.1155/2020/6591873

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().