Abstract
Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity. Therefore, thorough research is important in theory and application. Although numerous vehicle logo detection methods exist, most of them cannot achieve real-time detection for different scenes. The YOLOv2 network is improved by constructing the data of a vehicle logo, dimension clustering of the bounding box, reconstructing network pre-training, and multi-scale detection training. This work implements fast and accurate vehicle logo detection. The generalization of the detection model and anti-interference capability in real scenes are optimized by data enrichment. The experimental results show that the accuracy and speed of the detection algorithm are improved.
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Acknowledgments
This work is supported by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2015BAD29B01), Key Research Guidance Plan Project of Liaoning Province (No. 2017104013), Natural Science Foundation of Liaoning Province (No. 201700133), and Fundamental Research Funds of Central University (No. 0102-20000101).
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Yang, S., Bo, C., Zhang, J., Wang, M. (2020). Vehicle Logo Detection Based on Modified YOLOv2. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_8
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DOI: https://doi.org/10.1007/978-3-030-17763-8_8
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