用YOLOv8分两步自动识别结构图中辅助绝缘层是否存在
Inscanner: Dual-Phase Detection and Classification of Auxiliary Insulation Using YOLOv8 Models
- 先用YOLOv8x检测图纸中应有绝缘的区域
- 再将检测结果分类，准确率达98%
- 适合建筑质检与智能图纸审查场景
本研究提出一种两阶段方法，用于检测和分类结构构件中的辅助绝缘层。第一阶段在包含完整结构蓝图的数据集上训练YOLOv8x模型，每张图纸均标注了应含绝缘的区域边界框；第二阶段将检测出的绝缘区域裁剪并分为“存在”或“缺失”两类，再用YOLOv8x-CLS模型进行分类。两个数据集均经过标注、增强和区域裁剪预处理。检测模型mAP达82%，分类模型准确率达98%。结果表明该方法在自动化绝缘检测与分类方面有效，为后续发展奠定基础。
原文摘要 · Abstract (English)
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.
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