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Real-Time Speed Breaker Detection with an Edge Impulse

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Abstract

Excessive vehicle speed is a leading cause of traffic accidents, prompting the strategic installation of speed breakers on roads to mitigate risks in vulnerable areas. These road features play a crucial role in curbing vehicle speeds, ultimately bolstering pedestrian safety. Recognizing the urgency in addressing this concern, the real-time detection of speed breakers becomes imperative for providing timely alerts to drivers. The proposed solution relies on machine learning (ML), demonstrating effectiveness in identifying both marked and unmarked speed breakers under challenging conditions such as faded images, dust, tree shadows and variable street lighting, which are especially crucial during nighttime. By integrating the Arduino Nano 33 BLE and Tiny ML, this system ensures speed breaker recognition, autonomous data collection and delivers instantaneous alerts. This holistic implementation markedly improves on-road safety for drivers. The proposed work is compared with several algorithms and compared to performancemeasures, such as accuracy, precision, recall the results are satisfactory. Finally, a maximum accuracy of 92% was achieved.Using this approach, it was possible to detect speed breaker with a precision of 82% and recall of 94.%.

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Data Availability

The dataset used and analyzed in this study is available upon request from the corresponding author.

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Acknowledgements

The authors warmly acknowledged the Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India for providing the facilities required to carry out the research.

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No funding received for this research.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

    S. Manjula, M. Swarna Sudha, C. A. Yogaraja & C. Muthuaruna

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  1. S. Manjula
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  2. M. Swarna Sudha
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  3. C. A. Yogaraja
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  4. C. Muthuaruna
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Manjula, S., Sudha, M.S., Yogaraja, C.A. et al. Real-Time Speed Breaker Detection with an Edge Impulse. SN COMPUT. SCI. 5, 766 (2024). https://doi.org/10.1007/s42979-024-03132-5

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