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Traffic signs and markings recognition based on lightweight convolutional neural network

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Abstract

Intelligent recognition of traffic signs and markings is an important component of autonomous driving and intelligent transportation systems. It is also an important theoretical basis for autonomous driving path planning. To address the low accuracy and poor real-time performance of the traffic signs and markings detection in complex and multivariate scenes, a lightweight convolutional neural network recognition method in multiple interference scenes is proposed. Firstly, based on Gamma correction and contrast limited histogram equalization algorithm, the traffic signs and markings image under the low illumination condition is enhanced adaptively. Then, the MobileNet-V2 fuse together with DeepLab-V3 + algorithm to segment traffic signs and markings in multiple scenes. Finally, an identification model is established to realize the adaptive recognition of traffic signs and markings, which are based on Lightweight Convolutional Neural Networks (Lw-CNN). Besides, the recognition method proposed in this paper is verified by the CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB). The experimental results show that the Mean Intersection Over Union (MIOU) of the MobileNet-V2 and DeepLab-V3 + algorithm reaches 83.07%, it increased by 25.8% than before image enhancement. The recognition accuracy of algorithm based on lightweight convolutional neural network is 99.92%, higher than the algorithms of MobileNet-V2 and VGG16.

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Some or all of the code used during the study are available on request from the corresponding author.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (52072054), the Special Key Project of Chongqing Technology Innovation and Application Development (cstc2021jscx-cylh0026), and the Open Funding Key Laboratory of Industry and Information Technology (2021KFKT01).

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  1. School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, China

    Shuen Zhao, Zhikun Gong & Dongyu Zhao

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  1. Shuen Zhao
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  2. Zhikun Gong
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  3. Dongyu Zhao
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Correspondence to Zhikun Gong.

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Zhao, S., Gong, Z. & Zhao, D. Traffic signs and markings recognition based on lightweight convolutional neural network. Vis Comput 40, 559–570 (2024). https://doi.org/10.1007/s00371-023-02801-5

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