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CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5

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Abstract

Realizing real-time and robust crosswalk (zebra crossing) detection in complex scenarios and under limited computing power is one of the important difficulties of current intelligent traffic management systems (ITMS). Limited edge computing capabilities and real complex scenarios such as in cloudy, sunny, rainy, foggy and at night simultaneously challenge this task. In this study, the crosswalk detection network (CDNet) based on YOLOv5 is proposed to achieve fast and accurate crosswalk detection under the vision of the vehicle-mounted camera, and real-time detection is implemented on Jetson nano device. The powerful convolution neural network feature extractor is used to handle complex environments, the squeeze-and-excitation (SE) attention mechanism module is embedded into the network, the negative samples training (NST) method is used to improve the accuracy, the region of interest (ROI) algorithm is utilized to further improve the detection speed, and a novel slide receptive field short-term vector memory (SSVM) algorithm is proposed to improve vehicle-crossing behavior detection accuracy, the synthetic fog augmentation algorithm is used to allow the model adaptable to foggy scenario. Finally, with a detection speed of 33.1 FPS on Jetson nano, we obtained an average F1 score of 94.83% in the above complex scenarios. For better weather condition such as sunny and cloudy days, the F1 score exceeds 98%. This work provides a reference for the specific application of artificial neural network algorithm optimization methods on edge computing devices. The datasets, tutorials and source codes are available on GitHub.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Numbers: 61873163]. We also acknowledge the Center for High Performance Computing at Shanghai Jiao Tong University for providing computing resources.

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zheng-De Zhang, Hai-Chun Liu, Ling Pei & Wen-Xian Yu

  2. Shanghai West Hongqiao Navigation Technology Ltd, Shanghai, 201702, China

    Zheng-De Zhang, Zhi-Cai Lan, Hai-Chun Liu & Wen-Xian Yu

  3. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China

    Meng-Lu Tan

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Meng-Lu Tan

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  1. Zheng-De Zhang
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  4. Hai-Chun Liu
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  5. Ling Pei
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Contributions

Zhengde Zhang contributed to conceptualization, methodology, software, formal analysis, data curation, writing—original draft and visualization. Menglu Tan contributed to methodology, data curation, writing—original draft and writing—review and editing. Zhicai Lan contributed to conceptualization, investigation and writing—review and editing. Haichun Liu contributed to validation and investigation. Ling Pei contributed to resources, funding acquisition and methodology. Wenxian Yu contributed to resources, supervision, funding acquisition and project administration.

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Correspondence to Zheng-De Zhang or Wen-Xian Yu.

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Zhang, ZD., Tan, ML., Lan, ZC. et al. CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5. Neural Comput & Applic 34, 10719–10730 (2022). https://doi.org/10.1007/s00521-022-07007-9

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