Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rout, Ivan Sunit; Pandian, P. Pal; Raj, Anil; Rego, Anil Melwyn; Panigrahi, Sajna Parimita Title A name given to the resource Semantic segmentation for data validation in unmanned robotic vehicles Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived IAES International Journal of Robotics and Automation;Volume;15;Issue;1;pp.71-79 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijra.v15i1.pp71-79" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijra.v15i1.pp71-79</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036834673?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036834673?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Dataset; DeepLab; Imaging semantic segmentation; OpenCV; Unmanned robotic vehicles Publisher An entity responsible for making the resource available Intelektual Pustaka Media Utama Relation A related resource ISSN: 20894856; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online