Advanced data augmentation techniques to enhance instance segmentation dataset for construction and demolition waste management

Data annotation is a significant bottleneck in compiling instance segmentation datasets, particularly in the context of construction and demolition waste. Data augmentation has been shown to address this issue by increasing the diversity, instances, and complexities of data. While augmentation techniques for detection datasets are well-documented, a comprehensive evaluation of methods to improve the robustness of instance segmentation models is lacking. In this study, we developed and evaluated various data augmentation techniques on a publicly available dataset. Our findings indicate a 6% increase in mask prediction accuracy using the class balance method and a 4% improvement when combining real and synthetic data for training. Additionally, the mask prediction accuracy for minority classes increased by 30% using the augmentation techniques. This study demonstrates practical augmentation techniques to enhance instance segmentation performance with adaptation capability in any instance segmentation dataset within the context of waste management.