Research on the Technology of Rapid Detection of Dam Seepage Areas Using Infrared Grayscale Images Based on Mask R-CNN Model
With the increasingly severe safety issues of dam bodies in hydraulic engineering,how to efficiently and accurately detect the seepage area of dam bodies has become the key to ensuring the structural safety of dam bodies.Infrared grayscale images have gradually become an important tool for detecting water seepage areas due to their sensitivity to temperature changes.This article proposes a rapid detection method for seepage areas in infrared gray-scale images of dam bodies based on Mask Region-Based Convolutional Neural Network(Mask R-CNN).This method achieves precise localization and pixel level segmentation of seepage areas through core technologies such as Region Proposal Network(RPN)and RoIAlign,and combines noise suppression and image enhancement tech-niques to further improve the accuracy and robustness of detection.Through comparative experiments with tradi-tional algorithms,the superiority of the proposed method in detecting complex backgrounds has been verified.The experimental results show that the Mask R-CNN model not only significantly outperforms traditional methods such as threshold segmentation and edge detection in terms of accuracy and detection efficiency,but also has stron-ger adaptability in capturing boundary details of water seepage areas.
万方数据
维普
