TrueDeep: A systematic approach of crack detection with less data
This addresses the challenge of high annotation costs for crack detection in infrastructure monitoring, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of expensive data annotation in crack detection by incorporating domain knowledge to select input images, achieving similar performance with only 23% of the data, as measured by mIoU and F score on test and blind datasets.
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 algorithms, developed with 23% of the overall data; have a similar performance on the test data and significantly better performance on multiple blind datasets.