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All Journal IAES International Journal of Robotics and Automation (IJRA)
Rout, Ivan Sunit
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Automotive Engineering Electrical & Electronics Engineering

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Semantic segmentation for data validation in unmanned robotic vehicles Rout, Ivan Sunit; Pandian, P Pal; Raj, Anil; Rego, Anil Melwyn; Panigrahi, Sajna Parimita
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp71-79

Abstract

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.
Co-Authors Pandian, P Pal Panigrahi, Sajna Parimita Raj, Anil Rego, Anil Melwyn