Duo Ma
- Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images. In: Computer-Aided Civil and Infrastructure Engineering. :
- Study on the shear strength and damage constitutive model of the contact surface between PVA fiber-enhanced cement mortar and concrete. In: Construction and Building Materials, v. 400 (October 2023). (2023):
- An automatic defect classification and segmentation method on three-dimensional point clouds for sewer pipes. In: Tunnelling and Underground Space Technology, v. 143 (January 2024). (2024):
- Defects identification and location of underground space for ground penetrating radar based on deep learning. In: Tunnelling and Underground Space Technology, v. 140 (October 2023). (2023):
- A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines. In: Tunnelling and Underground Space Technology, v. 141 (November 2023). (2023):
- 3D reconstruction and segmentation system for pavement potholes based on improved structure-from-motion (SFM) and deep learning. In: Construction and Building Materials, v. 398 (September 2023). (2023):
- Transformer‐optimized generation, detection, and tracking network for images with drainage pipeline defects. In: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 15 (April 2023). (2023):
- Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects. In: Automation in Construction, v. 144 (December 2022). (2022):
- Automatic damage segmentation in pavement videos by fusing similar feature extraction siamese network (SFE-SNet) and pavement damage segmentation capsule network (PDS-CapsNet). In: Automation in Construction, v. 143 (November 2022). (2022):
- Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion. In: Construction and Building Materials, v. 324 (March 2022). (2022):
- A Multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN. In: Construction and Building Materials, v. 312 (December 2021). (2021):