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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Yohanes Yohanie Fridelin Panduman
The University of Osaka
Computer Science & IT Engineering

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Iron welding spot segmentation using Nested UNet (UNet++) enhanced with diverse convolutional modules Thomas Brian; Oskar Natan; Yohanes Yohanie Fridelin Panduman; Anggarjuna Puncak Pujiputra
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.2250

Abstract

Welding inspection plays an essential role in manufacturing industries to ensure the integrity and quality of weld joints. However, the prevalent manual inspection procedures are inherently subjective, prone to bias, and result in inconsistent quality assessments. Therefore, there is a strong need for an automated, intelligent system capable of objectively detecting welding spots. To address this, we propose an advanced segmentation model based on deep learning and computer vision techniques, specifically utilizing a Nested UNet (UNet++) architecture enhanced by extensive architectural modifications and comprehensive hyperparameter tuning. To further optimize segmentation performance, we systematically compare various convolutional blocks integrated into the bottleneck of the network architecture. Our experimental evaluation demonstrates that employing a VGG convolutional block at the bottleneck of Nested UNet achieves the highest performance, reaching an Intersection over Union (IoU) score of 76.18% and a validation loss of 0.1713 on our collected dataset. This research advances automated welding inspection by demonstrating that an enhanced Nested UNet with an optimized VGG bottleneck significantly improves weld spot segmentation accuracy. The proposed framework provides a robust benchmark for future studies on intelligent industrial visual inspection and deep learning-based quality assurance.
Evaluating YOLOv8-pose variants for soccer keypoint detection and homography mapping Durrotun Nashihin; Yohanes Yohanie Fridelin Panduman; Dwi Cahyo Kartiko; Anna Noordia; Khoirul Islam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4819-4829

Abstract

Soccer analytics intensely relies on accurate pitch keypoint detection and homography mapping, yet soccer broadcast data poses challenges caused by camera angle variation and partial pitch coverage. This study systematically evaluates you only look once version 8 (YOLOv8)-pose variants (nano, small, medium, large, and x-large) for keypoint detection and homography mapping. These models were retrained on broadcast and scouting datasets, while homography mapping was computed using direct linear transform (DLT) and random sample consensus (RANSAC). Three metrics were used for evaluation: detection accuracy, computational efficiency, and homography reliability. Results showed that the medium variant achieved the best balance, with the lowest mean pixel error (35.67 px), competitive inference speed 49.6 frames per second (FPS), and moderate model size (52.2 MB). Furthermore, it also reached the highest homography success rate (96.2%), surpassing all other variants. These findings suggest that the medium variant is the most effective balance for real-time soccer analytics, providing robust performance in accuracy, efficiency, and reliability under diverse broadcast data. Future studies would focus on enlarging the dataset with additional keypoints, incorporating temporal smoothing, and integrating a player tracking system. For reproducibility, the dataset and code are openly available on GitHub.
Co-Authors Anggarjuna Puncak Pujiputra Anna Noordia Durrotun Nashihin Dwi Cahyo Kartiko Khoirul Islam Oskar Natan Thomas Brian