Abstract
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.
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References
Huimin, L., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. MONET 23(2), 368–375 (2018)
Li, P., Wang, D., Wang, L., Lu, H.: Deep visual tracking: review and experimental comparison. Pattern Recogn. 76, 323–338 (2018)
Mao, S., Ye, M., Li, X., Pang, F., Zhou, J.: Rapid vehicle logo region detection based on information theory. Comput. Electr. Eng. 39(3), 863–872 (2013)
Bianco, S., Buzzelli, M., Mazzini, D., Schettini, R.: Logo recognition using CNN features. In: International Conference on Image Analysis and Processing, pp. 438–448 (2015)
Liao, Y., Lu, X., Zhang, C., Wang, Y., Tang, Z.: Mutual enhancement for detection of multiple logos in sports videos. In: IEEE International Conference on Computer Vision, pp. 4856–4865 (2017)
Du, S., Ibrahim, M., Shehata, M., et al.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311–325 (2013)
Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000. Better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517–6525 (2017)
Acknowledgements
This work is supported by Key Research Guidance Plan Project of Liaoning Province (No. 2017104013), Natural Science Foundation of Liaoning Province (No. 201700133).
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Yang, S., Bo, C., Zhang, J., Wang, M., Chen, L. (2020). A New Dataset for Vehicle Logo Detection. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_17
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DOI: https://doi.org/10.1007/978-3-030-04946-1_17
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