- Automated pavement detection and artificial intelligence pavement image data processing technology. In: Automation in Construction, v. 168 (December 2024). (2024):
- Intelligent detection and mileage positioning of multiple distresses using two-step deep learning. In: Automation in Construction, v. 166 (October 2024). (2024):
- Intelligent Pixel-Level Rail Running Band Detection Based on Deep Learning. In: Journal of Infrastructure Systems, v. 30, n. 3 (September 2024). (2024):
- Pixel-level automatic detection and quantification of running bands on rail surfaces. In: Automation in Construction, v. 165 (September 2024). (2024):
- Automated instance segmentation of asphalt pavement patches based on deep learning. In: Structural Health Monitoring. :
- Intelligent detection of fastener defects in ballastless tracks based on deep learning. In: Automation in Construction, v. 159 (March 2024). (2024):
- Investigation on mesoscopic pore characteristics in asphalt mixtures under the coupling effects of Segregation-Dynamic pore Water-Salt corrosion based on CT scanning technology. In: Construction and Building Materials, v. 404 (November 2023). (2023):
- Intelligent paving and compaction technologies for asphalt pavement. In: Automation in Construction, v. 156 (December 2023). (2023):
- Pixel-level detection of multiple pavement distresses and surface design features with ShuttleNetV2. In: Structural Health Monitoring, v. 23, n. 2 (July 2023). (2023):
- Automated Detection of Pavement Manhole on Asphalt Pavements with an Improved YOLOX. In: Journal of Infrastructure Systems, v. 29, n. 4 (December 2023). (2023):
- Automated Pixel-Level Detection of Expansion Joints on Asphalt Pavement Using a Deep-Learning-Based Approach. In: Structural Control and Health Monitoring, v. 2023 (February 2023). (2023):
- Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks. In: Computer-Aided Civil and Infrastructure Engineering, v. 38, n. 18 (14 November 2023). (2023):
- Policy Gradient–Based Focal Loss to Reduce False Negative Errors of Convolutional Neural Networks for Pavement Crack Segmentation. In: Journal of Infrastructure Systems, v. 29, n. 1 (March 2023). (2023):
- Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements. In: Computer-Aided Civil and Infrastructure Engineering, v. 37, n. 13 (August 2022). (2022):
- Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network. In: Journal of Computing in Civil Engineering, v. 35, n. 1 (January 2021). (2021):