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Yu et al. [8] introduced a multi-image-featured-based method for detecting cracks on concrete using an enhanced salp swarm algorithm applied to a support vector machine to improve the accuracy of crack detection. Additionally, Dong et al. [9] used encoder-decoder networks to perform pixel-level fatigue crack segmentation for steel structures. CrackForest [10] continues to identify cracks using handcrafted features, although they are insufficiently discriminative to distinguish fractures from complicated backgrounds with low level signals. Similarly, Li et al. [11] proposed a multiscale image function to detect cracks on roadway images.

Recently, Sobol et al. [12] and Park et al. [13] applied a variety of fully convolutional networks (FCNs) in order to detect cracks in pavement. These were trained in an end-to-end approach for pixel-level segmentation and detection. However, when processing some images, the network went into a state in which all pixels were processed as a background; a similar problem was also described in [14], where the method could not detect thin cracks. Oliveilra et al. [15] introduced a method that is based on support vector machines and applied it to pavement images where they were frequently broken into a succession of sub-depicted images during training. However, the method could not precisely segment out fracture curves throughout the entire image because the result is often only a label for the sub-image. Zou et al. [16] and Yang et al. [17] presented DCNNs for crack detection with hierarchical feature learning to identify pavement deterioration.

The abovementioned methods have good results in the automatic detection of road surface cracks. However, these methods have a high rate of false-positive results on images taken in real-world. In other words, images captured by a camera in real-world settings without any post-processing. These images have several defects that increase false-positive results, such as:

Cracks are detected with 0% accuracy by FCN-VGG. There are no cracks observed in samples 1 and 3, as shown in Figure 7. As a result, when the fractures are narrow, the rate of false negatives is considerable. It can be explained due to higher complexity of input images used in this paper as images captured by a camera in real-world settings without any post-processing.

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