TrueDeep: A systematic approach of crack detection with less data
Ram Krishna Pandey Akshit Achara
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.