A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pavement anomaly detection based on transformer and self-supervised learning
Abstract Pavement anomaly detection can help reduce the pressure of data storage, transmission, labelling and processing. This paper describes a novel method based on transformer and self-supervised learning that assists in locating anomaly sections. Experimental results reveal that self-supervised learning can improve performance on a small dataset with unlabeled images. Transformer is proven to be applicable in the pavement distress detection field. The facial recognition-like framework we built can enhance the performance without training by putting new patches into the gallery. Removing similar patches does not affect the recognition results. The method is sufficiently efficient and miniaturized to support real-time work and can be applied directly to edge detection.
Highlights A novel method to detect anomaly pavement is proposed. Self-supervised learning is an efficient way to boost the model's generalization. A facial recognition-like framework can improve accuracy without training.
Pavement anomaly detection based on transformer and self-supervised learning
Abstract Pavement anomaly detection can help reduce the pressure of data storage, transmission, labelling and processing. This paper describes a novel method based on transformer and self-supervised learning that assists in locating anomaly sections. Experimental results reveal that self-supervised learning can improve performance on a small dataset with unlabeled images. Transformer is proven to be applicable in the pavement distress detection field. The facial recognition-like framework we built can enhance the performance without training by putting new patches into the gallery. Removing similar patches does not affect the recognition results. The method is sufficiently efficient and miniaturized to support real-time work and can be applied directly to edge detection.
Highlights A novel method to detect anomaly pavement is proposed. Self-supervised learning is an efficient way to boost the model's generalization. A facial recognition-like framework can improve accuracy without training.
Pavement anomaly detection based on transformer and self-supervised learning
Lin, Zijie (author) / Wang, Hui (author) / Li, Shenglin (author)
2022-08-22
Article (Journal)
Electronic Resource
English
Transformer encoder based self-supervised learning for HVAC fault detection with unlabeled data
| Elsevier | 2024
Pavement crack detection based on transformer network
| Elsevier | 2022
Data Augmentation by an Additional Self-Supervised CycleGAN-Based for Shadowed Pavement Detection
| DOAJ | 2022