TrueDeep: A systematic approach of crack detection with less data

Abstract

Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either not available or the annotation is expensive. The objective of this work is to show that by incorporating domain knowledge along with deep learning architectures, we can achieve similar performance with less data. We have used publicly available crack segmentation datasets and shown that selecting the input images using knowledge can significantly boost the performance of deep-learning based architectures. Our proposed approaches have many fold advantages such as low annotation and training cost, and less energy consumption. We have measured the performance of our algorithm quantitatively in terms of mean intersection over union (mIoU) and F-score. Our representative model, developed with 23% of the overall data; surpasses the baseline model (>1.5% F-score gain) on the test data while matching the benchmark performance and, with a suitable augmentation strategy, outperforms the baseline model (11.5% average F-score gain) and the benchmark (>8.75% average F-score gain) over the selected blind datasets.