Inscanner: Dual-Phase Detection and Classification of Auxiliary Insulation Using YOLOv8 Models
Youngtae Kim, Soonju Jeong, Sardar Arslan, Dhananjay Agnihotri, Yahya Ahmed, Ali Nawaz, Jinhee Song, Hyewon Kim
TL;DR
Addresses the challenge of automated insulation verification in industrial blueprints by replacing manual inspection with AI-driven analysis. Introduces a two-phase pipeline: detection of insulation regions in full-resolution blueprints with YOLOv8x, followed by classification of cropped patches with YOLOv8x-CLS to determine presence vs absence. The dataset is extensively augmented and rigorously validated by more than ten industry experts, achieving a detection mAP of $82%$ and a classification accuracy of $98%$. This approach offers scalable, accurate insulation verification to enhance industrial quality assurance and safety.
Abstract
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.