Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Classification

Abstract

Deep learning breakthrough stimulates the new research trend in infrastructure defects inspection. The lack of quality-controlled, human-annotated, free of charge, and publicly available defect datasets with sufficient amounts of data hinders the progress of deep learning in defects inspection. To boost research in deep learning-based defects inspection, we first summarize 37 publicly available defect datasets in this two-part survey. These defect datasets cover common defects in various types of infrastructure, and the taxonomy of the datasets is based on specific deep learning objectives. Besides, taking crack as the research target, we have combined the existing datasets with self-labeled crack images to establish a benchmark dataset for crack classification and segmentation. Moreover, based on the established crack dataset, we make a comprehensive comparison between state-of-the-art algorithms for classification, segmentation, and detection. In this paper, we concentrate on datasets and algorithms for defect classification. Altogether 11 classification-oriented defect datasets are summarized and demonstrated in detail. Based on the established crack dataset, we comprehensively compare existing state-of-the-art algorithms for object classification, which provides a baseline for future research in defects inspection. The companion paper of this work surveys datasets and algorithms for defect segmentation and detection, where 26 defect datasets are elaborated, and systematic comparison between state-of-the-art algorithms has been conducted. The classification algorithms illustrated in this paper are conducive to the coarse localization of the defects' position in images.