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Traffic sign detection and recognition under low illumination

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Abstract

To address the problems such as the difficulty of traffic sign detection and recognition under low illumination, a new low illumination traffic sign detection and recognition algorithm is proposed. The algorithm firstly uses an illumination judgement algorithm to filter out low-illumination images, then uses a New Illumination Enhancement algorithm to adjust the brightness and contrast of the low-illumination images, and finally uses mask RCNN (mask region-based convolutional neural network, mask RCNN) to detect and recognize traffic signs. The New Illumination Enhancement Algorithm is based on Illumination-Reflection model, firstly converting the image RGB space into HSV space, applying guided filtering to the V channel to obtain the illumination component, using the illumination component to extract the reflection component, and adjusting the reflection component by linear pull-up. Next, the distribution characteristics of the illumination component are used to adjust the 2D gamma function and obtain the optimized illumination component. Subsequently, the illumination component is used to obtain the detail component. Finally, a hybrid spatial enhancement method is used to obtain the enhanced V-channel and reconstruct the image. The experimental results show that the New Illumination Enhancement algorithm can effectively improve image brightness and sharpness in both low illumination traffic scenes, ensure that the image is not distorted, retain image information and enhance the prominence of traffic signs in traffic scenes. In the ZCTSDB-lightness test set, the combined algorithm of new light image enhancement and Mask RCNN improved object detection \({\varvec{mAP}}^{{{\varvec{bb}}}}\) and instance segmentation \({\varvec{mAP}}^{{{\varvec{seg}}}}\) by 2.810% and 1.176%, respectively, compared to Mask RCNN. In the ZCTSDB test set, the performance of the new low illumination traffic sign detection and recognition algorithm outperformed all other algorithms.

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Data sharing not applicable to this article as no datasets were generated during the current study.

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Funding

Transportation Technology Foundation of Zhejiang Province (202206).

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Authors and Affiliations

  1. Zhejiang University of Science and Technology, Zhejiang, 310023, Hangzhou, China

    Bingqiang Huang & Xinjian Xiang

  2. Zhejiang Scientific Research Institute of Transport, Zhejiang, 310009, Hangzhou, China

    Jiana Yao, Song Yang & Zhigang Lu

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  1. Jiana Yao
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  2. Bingqiang Huang
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Correspondence to Xinjian Xiang.

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Yao, J., Huang, B., Yang, S. et al. Traffic sign detection and recognition under low illumination. Machine Vision and Applications 34, 75 (2023). https://doi.org/10.1007/s00138-023-01417-y

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