智能城市2026，Vol.12Issue(1)：1-6,6.DOI:10.19301/j.cnki.zncs.2026.01.001
基于耦合模型的古建筑屋顶破损检测研究
Research on roof damage detection of ancient buildings based on coupled models
摘要
Abstract
To address the issues of missed and false detections in large-scale remote sensing images and dense scenes during the inspection of roof damage in ancient buildings,this paper proposes a Mask R-CNN YOLO v8 Pro hybrid detection model.The model integrates Mask R-CNN with an improved YOLO v8 by inputting the ancient building images extracted by Mask R-CNN into YOLO v8,and incorporating a large-scale detection head and shuffle attention(SA)mechanism in the YOLO v8 network to enhance the model's ability to extract small target features and focus on key information.The model was trained and tested on a self-made dataset with comparative experiments.The results show that for detecting four types of roof damage(holes,root erosion,debris,and displaced tiles),Mask R-CNN YOLO v8 Pro achieved an mAP@0.5 of 64.9％,outperforming conventional single detection models.Compared with other coupled models(Mask R-CNN YOLO v5,Mask R-CNN YOLO v8),mAP@0.5 increased by 13.3％and 12.1％,respectively.The study demonstrates that Mask R-CNN YOLO v8 Pro can more effectively extract feature information of small targets and focus on regions of interest in images,thereby reducing instances of missed and false detections.关键词
古建筑屋顶/破损检测/耦合模型/检测模型/分割模型Key words
ancient building roofs/damage detection/coupled model/detection model/segmentation model分类
信息技术与安全科学引用本文复制引用
兰楷,万程辉,喻文杰,陈安邦,李凤慧..基于耦合模型的古建筑屋顶破损检测研究[J].智能城市,2026,12(1):1-6,6.基金项目
江西省自然科学基金资助项目(20242BAB25199) （20242BAB25199）
