An active learning framework featured Monte Carlo dropout strategy for deep learning-based semantic segmentation of concrete cracks from images

Abstract

Training a deep learning model is always challenging as the data annotation requires expert knowledge, and is time consuming and laborious. To address this issue, the authors formulate an active learning framework to facilitate the training of deep learning models for performing concrete crack segmentation from images. The Monte Carlo dropout (MCDO) strategy, which requires no modification of deep learning models, is adopted to develop the uncertainty-based method to aid estimating the concrete crack features that the deep learning models are uncertain of, that is, feature representations that have not been well learned. Then, the informative data, that is, concrete crack images associated with high uncertainty score, are identified and retrieved for subsequent model training and optimization. The aforementioned processes can be repeated until all instances in the data pool are completely annotated or the target performance is attained. The feasibility of the proposed active learning framework is validated using an open-source concrete crack dataset. With only about 25% of training data, the deep learning model attains an intersection over union (IoU) of 0.930, which is about 99.2% of the score trained with all the training data (10,000 concrete crack images), demonstrating the capability of using sufficient amount of informative data to attain a promising result in concrete crack segmentation from images.