X-CDNet: A real-time crosswalk detector based on YOLOX

As urban traffic safety becomes increasingly important, real-time crosswalk detection is playing a critical role in the transportation field. However, existing crosswalk detection algorithms must be improved in terms of accuracy and speed. This study proposes a real-time crosswalk detector called X-...

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Bibliographic Details
Published in:Journal of visual communication and image representation 2024-06, Vol.102, p.104206, Article 104206
Main Authors: Lu, Xingyuan, Xue, Yanbing, Wang, Zhigang, Xu, Haixia, Wen, Xianbin
Format: Article
Language:English
Subjects:
CD9K
Crosswalk detection
Reparameterization
X-CDNet
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Summary:As urban traffic safety becomes increasingly important, real-time crosswalk detection is playing a critical role in the transportation field. However, existing crosswalk detection algorithms must be improved in terms of accuracy and speed. This study proposes a real-time crosswalk detector called X-CDNet based on YOLOX. Based on the ConvNeXt basic module, we designed a new basic module called Reparameterizable Sparse Large-Kernel (RepSLK) convolution that can be used to expand the model’s receptive field without the addition of extra inference time. In addition, we created a new crosswalk dataset called CD9K, which is based on realistic driving scenes augmented by techniques such as synthetic rain and fog. The experimental results demonstrate that X-CDNet outperforms YOLOX in terms of both detection accuracy and speed. X-CDNet achieves a 93.3 AP50 and a real-time detection speed of 123 FPS. •Proposed a new basic module, RepSLK, for constructing backbone and neck networks.•Constructed a new real-time crosswalk detection model, X-CDNet.•Established a new crosswalk detection dataset, CD9K.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2024.104206