中国农业信息2025，Vol.37Issue(3)：59-69,11.DOI:10.12105/j.issn.1672-0423.20250305
基于改进的Mask R-CNN的农村建筑物智能识别方法
Intelligent recognition method for rural buildings based on improved Mask R-CNN
摘要
Abstract
[Purpose]This study proposes an improved Mask R-CNN-based method for instance segmentation of rural buildings,aiming to enhance identification efficiency and promote the deep integration of artificial intelligence in agricultural informatization.[Method]Employing high-resolution orthophotos captured by UAV platforms as the data source,a total of 1 548 building images were obtained after cropping and screening,with annotations completed using the Labelme tool.The structure of the instance segmentation algorithm was enhanced by replacing the backbone network in the Mask R-CNN model.This modification effectively improved the detection accuracy for rural building targets.[Result]On the test set,the improved Mask R-CNN model exhibited enhanced overall accuracy,improved detection performance at high IoU thresholds,and superior segmentation capability for rural buildings across different scales.It successfully enabled the automatic extraction of building outlines from remote sensing imagery,thereby effectively reducing the reliance on manual mapping and significantly boosting surveying efficiency.[Conclusion]The enhanced Mask R-CNN model improves both the detection accuracy and the fine-grained detail-capturing capability for rural buildings,contributing significantly to the advancement of intelligent mapping.关键词
人工智能/Mask R-CNN/农村建筑物/智能成图Key words
artificial intelligence(AI)/Mask R-CNN/rural buildings/intelligent mapping引用本文复制引用
胡锦源,阴紫薇,高毓欣,李盘盘,符家科,范冲..基于改进的Mask R-CNN的农村建筑物智能识别方法[J].中国农业信息,2025,37(3):59-69,11.基金项目
湖南省重点领域研发计划"城市建筑群安全风险监测和评估研究"(2023SK2012) （2023SK2012）
