This study proposes a two-phase methodology for detecting and classifying auxiliary insulation in structural components. In the detection phase, a YOLOv8x model is trained on a dataset of complete structural blueprints, each annotated with bounding boxes indicating areas that should contain insulation. In the classification phase, these detected insulation patches are cropped and categorized into two classes: present or missing. These are then used to train a YOLOv8x-CLS model that determines the presence or absence of auxiliary insulation. Preprocessing steps for both datasets included annotation, augmentation, and appropriate cropping of the insulation regions. The detection model achieved a mean average precision (mAP) score of 82%, while the classification model attained an accuracy of 98%. These findings demonstrate the effectiveness of the proposed approach in automating insulation detection and classification, providing a foundation for further advancements in this domain.
@article{arxiv.2502.18871,
title = {Inscanner: Dual-Phase Detection and Classification of Auxiliary Insulation Using YOLOv8 Models},
author = {Youngtae Kim and Soonju Jeong and Sardar Arslan and Dhananjay Agnihotri and Yahya Ahmed and Ali Nawaz and Jinhee Song and Hyewon Kim},
journal= {arXiv preprint arXiv:2502.18871},
year = {2025}
}