A New Dataset for Vehicle Logo Detection

This paper establishes a multilevel dataset for solving the vehicle logo detection task; we call it ‘VLD-30’. Vehicle logo detection is applied to the Intelligent Transport System widely, such as vehicle monitoring. As for the object detection algorithm of deep-learning, a good dataset can improve the robustness of it. Our dataset has a very high reliability by including analysis on various factors. In order to confirm the dataset performance, we use the typical target detection algorithm, such as Faster-RCNN and YOLO. The experimental results show that our dataset achieves significant improvements for the small object detection, and vehicle logo detection is potential to be developed.

[1]  Mao Ye,et al.  Rapid vehicle logo region detection based on information theory , 2013, Comput. Electr. Eng..

[2]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Raimondo Schettini,et al.  Logo Recognition Using CNN Features , 2015, ICIAP.

[4]  Chengcui Zhang,et al.  Mutual Enhancement for Detection of Multiple Logos in Sports Videos , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[5]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Huimin Lu,et al.  Brain Intelligence: Go beyond Artificial Intelligence , 2017, Mobile Networks and Applications.

[7]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[8]  Huchuan Lu,et al.  Deep visual tracking: Review and experimental comparison , 2018, Pattern Recognit..

[9]  Wael Badawy,et al.  Automatic License Plate Recognition (ALPR): A State-of-the-Art Review , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.