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The detection of cracks is a crucial task in monitoring structural health andensuring structural safety. The manual process of crack detection istime-consuming and subjective to the inspectors. Several researchers have triedtackling this problem using traditional Image Processing or learning-basedtechniques. However, their scope of work is limited to detecting cracks on asingle type of surface (walls, pavements, glass, etc.). The metrics used toevaluate these methods are also varied across the literature, making itchallenging to compare techniques. This paper addresses these problems bycombining previously available datasets and unifying the annotations bytackling the inherent problems within each dataset, such as noise anddistortions. We also present a pipeline that combines Image Processing and DeepLearning models. Finally, we benchmark the results of proposed models on thesemetrics on our new dataset and compare them with state-of-the-art models in theliterature.

CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks