2020/11/05 by Yongsheng Bai, Bai, Yongsheng, Halil Sezen +3 · 1 citation
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Non-Destructive Testing Techniques #Structural Health Monitoring Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.03098
openalex publication_date 2020/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.