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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Vehicle Logo Detection Based on Modified YOLOv2

verfasst von : Shuo Yang, Chunjuan Bo, Junxing Zhang, Meng Wang

Erschienen in: 2nd EAI International Conference on Robotic Sensor Networks

Verlag: Springer International Publishing

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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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Metadaten
Titel
Vehicle Logo Detection Based on Modified YOLOv2
verfasst von
Shuo Yang
Chunjuan Bo
Junxing Zhang
Meng Wang
Copyright-Jahr
2020
Buch
2nd EAI International Conference on Robotic Sensor Networks

Print ISBN: 978-3-030-17762-1

Electronic ISBN: 978-3-030-17763-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-17763-8_8

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