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Multi-Scale Vehicle Logo Detector

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Abstract

As the key information of vehicles, vehicle logo can assist in completing the identification of vehicle information. Therefore, the task of vehicle logo detection is of great practical significance. The existing object detection systems for vehicle logo detection cannot account for the detection accuracy of large and small-scale objects. Moreover, the accuracy of these methods can be further improved. In this study, we propose a new approach called multi-scale vehicle logo detector (SVLD), which is based on SSD. This method obtains better results than the current detection methods by setting the parameters of the preset boxes, changing the pre-training strategy, and adjusting the network structure. Experiments show that the proposed approach is better for multi-scale vehicle logo detection. Vehicle logos with large span size can be clearly detected, and the detection accuracy is substantially improved compared with those of other classic algorithms. For 512 × 512 input, SVLD obtains 3.1% improvement over the conventional methods and achieves a mean average precision (mAP) of 84.8% in the VLD-45 test set.

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Funding

This work is supported in part by National Natural Science Foundation of China (No. 61806037), in part by Natural Science Foundation of Liaoning Province (No.2019-MS-067), in part by Youth Technology Star Project of Dalian City (No. 2018RQ57), in part by Minzu Innovation Foundation of Liaoning Province (No.2020-MZLH-23).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, Liaoning, China

    Junxing Zhang, Lijun Chen, Chunjuan Bo & Shuo Yang

Authors
  1. Junxing Zhang
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  2. Lijun Chen
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  3. Chunjuan Bo
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  4. Shuo Yang
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Contributions

Conceptualization, J.Z. and L.C.; methodology, J.Z.; software, L.C. and Y.S.; validation, J.Z., L.C. and C.B.; formal analysis, L.C.; investigation, Y.S.; resources, J.Z.; data curation, J.Z. and L.C.; writing—original draft preparation, J.Z. and L.C.; writing—review and editing, J.Z. and C.B.; visualization, J.Z.; supervision, C.B.; project administration, J.Z. and C.B.; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Chunjuan Bo.

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Cite this article

Zhang, J., Chen, L., Bo, C. et al. Multi-Scale Vehicle Logo Detector. Mobile Netw Appl 26, 67–76 (2021). https://doi.org/10.1007/s11036-020-01722-0

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