An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows

Abstract

Detection and assessment of cracks in civil engineering structures such as roads, bridges, dams and pipelines are crucial tasks for maintaining the safety and cost-effectiveness of those concrete structures. With the recent advances in machine learning, the development of ANN- and CNN-based algorithms has become a popular approach for the automated detection and identification of concrete cracks. However, most of the proposed models are trained on images taken in ideal conditions and are only capable of achieving high accuracy when applied to the concrete images devoid of irregular illumination conditions, shadows, shading, blemishes, etc. An overview of challenges related to the automatic detection of concrete cracks in the presence of shadows is presented in this paper. In particular, difficulties associated with the application of deep learning-based methods for the classification of concrete images with shadows are demonstrated. Moreover, the limitations of the shadow removal techniques for the improvement of the crack detection accuracy are discussed.

Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

“…To improve the concrete crack detection accuracy, three options could be proposed: 1st option is to eliminate shadows through pre-processing the acquired images before applying machine learning algorithms for concrete crack detection. This approach does not work well and the drawbacks have been demonstrated in [ 25 ]. 2nd option is to improve the concrete crack image database with a large collection of images with shadows in challenging illumination conditions to improve the accuracy of existing deep learning networks, as demonstrated in this paper.…”

“…However, shadow removal is not a straightforward task. It has been shown that pre-processing of concrete crack images for shadow removal could lead to severe deterioration in image quality, leading to incorrectly classified images [ 25 ].…”

“…Although several researchers have tried to solve the problem of shadow removal; this task still remains a complex topic with moderate to good success [ 21 ]. Shadow removal in concrete crack images often severely impacts the quality of the digital image, which makes further crack detection very challenging [ 25 ].…”

RIIAnet: A Real-Time Segmentation Network Integrated with Multi-Type Features of Different Depths for Pavement Cracks

“…In practice, the pavement materials used in different road sections often vary, meaning that the depth, shape, continuity, and even contrast of cracks are significantly diverse. Hence, traditional edge-detection algorithms find it difficult to achieve ideal results on fractures in diverse environments [14,15].…”

“…With the development of deep learning technology, the ability of computer systems to recognize and process images has been greatly improved because of the use of DCNNs and some image techniques using deep learning [14,[16][17][18] are developed to detect cracks. The FCN [19] network is the first end-to-end semantic segmentation network that uses a fully convolutional neural network (FCN) architecture, and performs dense execution based on 1 × 1 convolution to reduce parameters and improve speed.…”