ItemArticle

Semantic segmentation for data validation in unmanned robotic vehicles

Title
Semantic segmentation for data validation in unmanned robotic vehicles
Creator
Rout, Ivan Sunit; Pandian, P. Pal; Raj, Anil; Rego, Anil Melwyn; Panigrahi, Sajna Parimita
Description
Semantic segmentation is a vital aspect of computer vision, widely used in fields such as autonomous driving, medical imaging, and industrial automation. Maintaining high-quality datasets is crucial for enhancing model accuracy and minimizing real-world errors. This paper focuses on developing a comprehensive data validation pipeline for semantic segmentation using OpenCV. The proposed framework integrates automated integrity checks, preprocessing techniques, and consistency verification to manage large-scale datasets effectively. Key validation processes include image quality assessment (detection of blurriness and noise), verification of annotation accuracy, class distribution analysis, and identification of anomalies. Additionally, OpenCV-powered preprocessing steps, such as image resizing, normalization, contrast optimization, and data augmentation, are applied to refine dataset quality for segmentation models. This paper also addresses scalability concerns associated with processing extensive datasets, introducing optimized batch handling and parallel validation techniques. By implementing a structured validation workflow, this research enhances the reliability, robustness, and overall effectiveness of semantic segmentation models, ensuring high-quality training data for deep learning applications. 2026, Intelektual Pustaka Media Utama. All rights reserved.
Source
IAES International Journal of Robotics and Automation;Volume;15;Issue;1;pp.71-79
Date
01-01-2026
Publisher
Intelektual Pustaka Media Utama
Subject
Dataset; DeepLab; Imaging semantic segmentation; OpenCV; Unmanned robotic vehicles
Coverage
Rout I.S., Department of Mechanical, Automobile, Robotics and Mechatronics, Christ University, Bengaluru, India; Pandian P.P., Department of Mechanical, Automobile, Robotics and Mechatronics, Christ University, Bengaluru, India; Raj A., Department of Mechanical, Automobile, Robotics and Mechatronics, Christ University, Bengaluru, India; Rego A.M., Department of Mechanical, Automobile, Robotics and Mechatronics, Christ University, Bengaluru, India; Panigrahi S.P., Department of Mechanical, Automobile, Robotics and Mechatronics, Christ University, Bengaluru, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20894856;
Format
online
Language
English
Type
Article

Citation

Rout, Ivan Sunit; Pandian, P. Pal; Raj, Anil; Rego, Anil Melwyn; Panigrahi, Sajna Parimita, “Semantic segmentation for data validation in unmanned robotic vehicles,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23092.

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