Automatic Generation of Segmented Labels for Road Anomaly Detection: An Application for Robotic Wheelchair

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G. Shanmugavel, Renangi Venkata Nivas, Vavintaparthi Venkata Sai Teja Swaroop, Shaik Anwar Babu, Sanampudi Siva Sai

Abstract

Foreground moving object segmentation is a fundamental problem in many computer vision applications. As a solution for foreground segmentation, background modelling has been intensively studied over past years and many effective algorithms have been developed. However, accurate foreground segmentation is still a difficult problem. Currently, most of the algorithms work solely within the colour space, in which the segmentation performance is prone to be degraded by a multitude of challenges, such as illumination changes, shadows, automatic camera adjustments, and colour camouflage. However, the acquisition of large-scale datasets with hand-labelled ground truth is time-consuming and labour-intensive, by using these methods often hard to implement in practice. The proposed method develops the solution of this problem for the task of drivable area and road anomaly segmentation by proposing a self-supervised learning approach. The proposed method can automatically generate segmentation labels for drivable areas and road anomalies. Then, we train RGB-D data based semantic segmentation neural networks and get predicted labels. We firstly develop a pipeline named Self-Supervised Label Generator (SSLG) to automatically label drivable areas and road anomalies. Then, we use the segmentation labels generated by the SSLG to train several RGB-D data-based semantic segmentation neural networks

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How to Cite
G. Shanmugavel, Renangi Venkata Nivas, Vavintaparthi Venkata Sai Teja Swaroop, Shaik Anwar Babu, Sanampudi Siva Sai. (2023). Automatic Generation of Segmented Labels for Road Anomaly Detection: An Application for Robotic Wheelchair. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(2), 385–393. https://doi.org/10.17762/turcomat.v14i2.13663
Section
Research Articles