Robust and Precise Sidewalk Detection with Ensemble Learning: Enhancing Road Safety and Facilitating Curb Space Management

The study highlights a robust ensemble model's effectiveness for accurate sidewalk detection, vital for both road safety and efficient curb space management. To evaluate the proposed ensemble model, three distinct datasets were utilized: Cityscapes, Ade20k, and the Boston Dataset. The results d...

Full description

Saved in:
Bibliographic Details
Published in2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) pp. 5092 - 5099
Main Authors Shihab, Ibne Farabi, Bhagat, Sudesh Ramesh, Sharma, Anuj
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.09.2023
Subjects
Aerospace electronics
Ensemble learning
Intelligent transportation systems
Resilience
Road safety
Robustness
Speckle
Online AccessGet full text

Cover

More Information
Summary:The study highlights a robust ensemble model's effectiveness for accurate sidewalk detection, vital for both road safety and efficient curb space management. To evaluate the proposed ensemble model, three distinct datasets were utilized: Cityscapes, Ade20k, and the Boston Dataset. The results demonstrated the superiority of the ensemble model over its individual components, as manifested by mIOU scores of 93.1%, 90.3%, and 90.6% on the Cityscapes, Ade20k, and Boston datasets respectively, in optimal conditions. Under exposure to various noise types, such as Salt-and-Pepper and Speckle, across low to high intensities, the model revealed a steady, controlled decline in performance. This stands in contrast to the rapid drop experienced by individual models. Overall, the model demonstrated robustness and dependability. The ensemble model's resilience and dependability make a strong case for its application in enhancing road safety through reliable sidewalk detection and efficient curb space management.
ISSN:2153-0017
DOI:10.1109/ITSC57777.2023.10422138