CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks
This work addresses the need for standardized evaluation in structural health monitoring by providing a benchmark for crack segmentation, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of crack detection across different surfaces by combining existing datasets into a unified benchmark called CrackSeg9k, and presents a pipeline that integrates image processing and deep learning models, achieving results compared to state-of-the-art models.
The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by combining previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions. We also present a pipeline that combines Image Processing and Deep Learning models. Finally, we benchmark the results of proposed models on these metrics on our new dataset and compare them with state-of-the-art models in the literature.