北京测绘2026，Vol.40Issue(8)：1152-1158,7.DOI:10.19580/j.cnki.1007-3000.2026040129
优化Mask R-CNN的测量标志检测与智能巡检
Survey mark detection and intelligent inspection based on an optimized Mask R-CNN
摘要
Abstract
To address the problems of low manual inspection efficiency for survey marks and the susceptibility of automatic tar-get recognition in complex field scenarios to interference from illumination variations,occlusions,and high background com-plexity,this paper proposed a survey mark detection and intelligent inspection method based on an optimized mask region-based convolutional neural network(Mask R-CNN).Built on the baseline Mask R-CNN framework,the proposed method adopted a hyperparameter optimization strategy to automatically search and optimize key hyperparameters in the training process,so as to improve the model's feature extraction and object detection capabilities under complex scenes.The optimized model was subse-quently deployed on a provincial-level natural resources inspection and management platform for practical application valida-tion.Experimental results show that the proposed method achieves a precision of 94.30%and an F1-score of 91.13%,exhibit-ing robust recognition performance and stability in complex environments.Following platform deployment,inspection effi-ciency has been significantly improved,and the risk of missed detections has been effectively reduced.This method can improve the level of intelligent survey mark recognition and inspection management,providing technical reference for the infor-matization and intelligentization of survey mark protection and inspection management workflows.关键词
测量标志/目标检测/掩膜区域卷积神经网络(Mask R-CNN)/超参数优化/智能巡检Key words
survey mark/object detection/mask region-based convolutional neural network(Mask R-CNN)/hyperparam-eter optimization/intelligent inspection分类
天文与地球科学引用本文复制引用
潘诗辰,徐攻博,王紫琦,严灏,陈铭鸿..优化Mask R-CNN的测量标志检测与智能巡检[J].北京测绘,2026,40(8):1152-1158,7.基金项目
浙江省自然资源厅科技项目(2024ZJHY001) （2024ZJHY001）
