Inscanner: Dual-Phase Detection and Classification of Auxiliary Insulation Using YOLOv8 Models

TL;DR

This paper introduces a two-phase deep learning approach using YOLOv8 models to automatically detect and classify auxiliary insulation in structural blueprints, achieving high accuracy and precision.

Contribution

It presents a novel dual-phase methodology combining detection and classification with YOLOv8 models for insulation analysis in structural blueprints.

Findings

Detection model achieved 82% mAP

Classification model attained 98% accuracy

Effective automation of insulation inspection processes

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.