The proposed method can be extended to handle other types of infrastructure defects beyond pavement cracks by adapting the network architecture and training process to suit the specific characteristics of the new defect types. Here are some ways to extend the method:
Data Augmentation: Collecting a diverse dataset that includes images of different types of infrastructure defects such as potholes, corrosion, or structural damage. By augmenting the dataset with various types of defects, the model can learn to detect and classify different types of defects effectively.
Feature Extraction: Modify the feature extraction layers of the network to capture the unique characteristics of different types of defects. For example, for detecting potholes, the network may need to focus on texture and depth information, while for corrosion detection, it may need to analyze patterns of degradation.
Labeling Strategy: Develop a labeling strategy that accounts for the specific attributes of each type of defect. This may involve creating detailed annotations for different defect classes to ensure accurate training and evaluation of the model.
Fine-tuning and Transfer Learning: Utilize transfer learning techniques to fine-tune the pre-trained model on a new dataset of infrastructure defects. By leveraging the knowledge learned from pavement crack detection, the model can adapt to new defect types more efficiently.
Multi-Task Learning: Implement a multi-task learning approach where the model is trained to detect multiple types of defects simultaneously. This can help improve the overall performance and generalization of the model across different defect categories.