无线电工程2025，Vol.55Issue(5)：966-974,9.DOI:10.3969/j.issn.1003-3106.2025.05.008
基于改进Mask R-CNN的水电站水下建筑物缺陷检测
Defect Detection of Underwater Buildings in Hydropower Station Based on Improved Mask R-CNN
摘要
Abstract
Defect detection of underwater buildings is one of the key tasks to ensure the long-term stable operation of power plants.To solve the problems of low visibility of underwater buildings,high cost of manual detection,dangerous detection tasks and low accuracy of defect detection,an underwater building defect detection algorithm based on improved Mask R-CNN is proposed.Firstly,image processing technology is used to improve the quality of underwater defect images;then K-mean clustering algorithm is used to determine the a priori bounding box to improve the efficiency of the model;the attention mechanism is added to the network to focus on the important information,enhance the network's attention to underwater defects,and improve the performance of the model and the accuracy of detection;and the feature fusion network is modified to SS-FPN(FPN Scale Sequence)to reduce the loss of information in the fusion of features and to enhance semantic fusion.Comparison test results indicate that compared with the ResNet50-based Mask R-CNN algorithm before improvement,the improved algorithm improves the detection accuracy of defects in underwater buildings of hydropower stations,and the defect contours obtained from subsequent processing are more accurate.关键词
水电站/水下建筑物/缺陷检测/Mask R-CNN/注意力机制/特征融合Key words
hydropower station/underwater building/defect detection/Mask R-CNN/attention mechanism/feature fusion分类
计算机与自动化引用本文复制引用
张福林,王思逸,彭望,何云,刘卫国..基于改进Mask R-CNN的水电站水下建筑物缺陷检测[J].无线电工程,2025,55(5):966-974,9.基金项目
国家能源集团江西电力有限公司科技创新项目资助(CEZB230604485)Supported by the Science&Technology Innovation Pro-gram of National Energy Group Jiangxi Electric Power Co.,Ltd.(CEZB230604485) （CEZB230604485）
