Implementing an Efficient Speed Bump Detection System Using Adaptive Threshold Gaussian over Support Vector Machine for Improved Detection
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Published
Feb 27, 2024
R. Priyanka
W. Deva Priya
Abstract
This research endeavours to identify speed bumps from provided images using Adaptive Thresholding for
enhanced detection. A total of 120 samples were divided equally into two groups. The first group, comprising
60 samples, underwent testing using the Support Vector Machine, while the second group was tested with the
Adaptive Threshold-Gaussian. Each group underwent 10 iterations. The dataset, comprising 6000 images
sourced from Kaggle.com, allocated 4800 images for training and the remaining for testing. With a G power
roughly at 80%, the Gaussian Adaptive Threshold yielded an accuracy of 85.60%, surpassing the Support
Vector Machine's 81.40%. A significance value of 0.002 (p<0.05) indicates that the results between the two
groups are statistically significant. The Gaussian Adaptive Threshold, therefore, stands out for its superior
accuracy.
How to Cite
R. Priyanka, & W. Deva Priya. (2024). Implementing an Efficient Speed Bump Detection System Using Adaptive Threshold Gaussian over Support Vector Machine for Improved Detection. SPAST Reports, 1(3). Retrieved from https://spast.org/ojspath/article/view/4864
Article Details
Keywords
Adaptive Threshold, Support Vector Machine, Gaussian Thresholding, Intelligent Vehicle System
References
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Varma, V. S. K. P., S. Adarsh, K. I. Ramachandran, and
Binoy B. Nair. 2018. “Real Time Detection of Speed
Hump/Bump and Distance Estimation with Deep
Learning Using GPU and ZED Stereo Camera.”
Procedia Computer Science 143 (January): 988–97.
V. P. Parandhaman, "An Automated Efficient and Robust
Scheme in Payment Protocol Using the Internet of
Things," 2023 Eighth International Conference on
Science Technology Engineering and Mathematics
(ICONSTEM), Chennai, India, 2023, pp. 1-5, doi:
10.1109/ICONSTEM56934.2023.10142797.
Zheng, Zengwei, Mingxuan Zhou, Yuanyi Chen, Meimei
Huo, and Dan Chen. 2019. “Enabling Real-Time Roads
Anomaly Detection via Mobile Edge Computing.”
International Journal of Distributed Sensor Networks
15 (11): 1550147719891319.
Adarsh. 2020. “Real-Time Speed Bump Detection
Using Image Segmentation for Autonomous Vehicles.”
In Advances in Intelligent Systems and Computing,
308–15. Advances in Intelligent Systems and
Computing. Cham: Springer International Publishing.
Ballinas-Hernández, Ana Luisa, Ivan Olmos-Pineda, and
José Arturo Olvera-López. 2022. “Marked and
Unmarked Speed Bump Detection for Autonomous
Vehicles Using Stereo Vision.” Journal of Intelligent &
Fuzzy Systems 42 (5): 4685–97.
Celaya-Padilla, Jose M., Carlos E. Galván-Tejada, F. E.
López-Monteagudo, O. Alonso-González, Arturo
Moreno-Báez, Antonio Martínez-Torteya, Jorge I.
Galván-Tejada, Jose G. Arceo-Olague, Huizilopoztli
Luna-García, and Hamurabi Gamboa-Rosales. 2018.
“Speed Bump Detection Using Accelerometric
Features, Gaussian Thresholding: A Genetic Algorithm
Approach.” Sensors 18 (2).
https://doi.org/10.3390/s18020443.
Cervantes, Jair, Farid Garcia-Lamont, Lisbeth RodríguezMazahua, and Asdrubal Lopez. 2020. “A
Comprehensive Survey on Support Vector Machine
Classification: Applications, Challenges and Trends.”
Neurocomputing 408 (September): 189–215.
Devapriya, W., C. Nelson Kennedy Babu, and T. Srihari.
2015. “Advance Driver Assistance System (ADAS) -
Speed Bump Detection.” In 2015 IEEE International
Conference on Computational Intelligence and
Computing Research (ICCIC), 1–6.
———. 2016. “Real Time Speed Bump Detection Using
Gaussian Filtering and Connected Component
Approach.” In 2016 World Conference on Futuristic
Trends in Research and Innovation for Social Welfare
(Startup Conclave), 1–5.
Dewangan, Deepak Kumar, and Satya Prakash Sahu. 2021.
“Deep Learning-Based Speed Bump Detection Model
for Intelligent Vehicle System Using Raspberry Pi.”
IEEE Sensors Journal 21 (3): 3570–78.
Elliott, Alan C., and Wayne A. Woodward. 2020. “Quick
Guide to IBM® SPSS®: Statistical Analysis With Stepby-Step Examples.”
https://doi.org/10.4135/9781071909638.
G. Ramkumar and M. Manikandan, "Uncompressed digital
video watermarking using stationary wavelet
transform," 2014 IEEE International Conference on
Advanced Communications, Control and Computing
Technologies, Ramanathapuram, India, 2014, pp.
Kosakowska, Katarzyna. 2022. “Evaluation of the Impact
of Speed Bumps on the Safety of Residents - Selected
Aspects.” Transportation Research Procedia 60
(January): 418–23.
Kishore Kumar, M. Aeri, A. Grover, J. Agarwal, P. Kumar,
and T. Raghu, “Secured supply chain management
system for fisheries through IoT,” Meas. Sensors, vol.
25, no. August 2022, p. 100632, 2023, doi:
10.1016/j.measen.2022.100632.
KuKuXia. . 13_Adaptive_thresholding.py at Master ·
KuKuXia/OpenCV_for_Beginners. Github. Accessed
December 27, 2022.
https://github.com/KuKuXia/OpenCV_for_Beginners.
Marques, Johny, Raulcezar Alves, Henrique C. Oliveira,
Marco MendonÇa, and Jefferson R. Souza. 2021. “An
Evaluation of Machine Learning Methods for SpeedBump Detection on a GoPro Dataset.” Anais Da
Academia Brasileira de Ciencias 93 (1): e20190734.
Varma, V. S. K. P., S. Adarsh, K. I. Ramachandran, and
Binoy B. Nair. 2018. “Real Time Detection of Speed
Hump/Bump and Distance Estimation with Deep
Learning Using GPU and ZED Stereo Camera.”
Procedia Computer Science 143 (January): 988–97.
Marques, Johny, Raulcezar Alves, Henrique C. Oliveira,
Marco MendonÇa, and Jefferson R. Souza. 2021. “An
Evaluation of Machine Learning Methods for SpeedBump Detection on a GoPro Dataset in driver assistance
system.” Anais Da Academia Brasileira de Ciencias 93
(1): e20190734.
Palanivelu, J., Thanigaivel, S., Vickram, S., Dey, N.,
Mihaylova, D., & Desseva, I. (2022). Probiotics in
functional foods: survival assessment and approaches
for improved viability. Applied Sciences, 12(1), 455.
Rokhim, Abdul, Hanendyo Putro, and Dani F. Brilianti.
2022. “Automatic Speed Bump (ASeB) to Reduce the
Rate of Traffic Accident in image processing” RSF
Conference Series: Engineering and Technology 2 (2):
79–87.
Varma, V. S. K. P., S. Adarsh, K. I. Ramachandran, and
Binoy B. Nair. 2018. “Real Time Detection of Speed
Hump/Bump and Distance Estimation with Deep
Learning Using GPU and ZED Stereo Camera.”
Procedia Computer Science 143 (January): 988–97.
V. P. Parandhaman, "An Automated Efficient and Robust
Scheme in Payment Protocol Using the Internet of
Things," 2023 Eighth International Conference on
Science Technology Engineering and Mathematics
(ICONSTEM), Chennai, India, 2023, pp. 1-5, doi:
10.1109/ICONSTEM56934.2023.10142797.
Zheng, Zengwei, Mingxuan Zhou, Yuanyi Chen, Meimei
Huo, and Dan Chen. 2019. “Enabling Real-Time Roads
Anomaly Detection via Mobile Edge Computing.”
International Journal of Distributed Sensor Networks
15 (11): 1550147719891319.
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