# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 2 Related Work  
### Vision Based Crack Detection Methods  
Many vision-based crack identification methods have already been suggested for solving the problem of manual inspection in the case of detecting cracks in concrete structures. One type of Computer Vision (CV)-based technique is the use of some traditional approaches. For example, [PERSON] et al. utilized a percolation-based image processing approach to identify cracks in concrete. The authors first extracted the cracks using the genetic programming method. Later, after calculating the crack tip, they used the high-precision percolation method to detect small cracks [7]. [PERSON] et al. proposed a Min-Max Gray Level Discrimination (M2 GLD) method to integrate with the Otsu method for detecting cracks in building structures. Their model also could find out a few crack characteristics, e.g., width, area, parameter [14]. [PERSON] et al. considered crack detection on noisy concrete structures. The authors used the subtraction pre-processing method for removing noises like shades and bad illumination conditions and introduced the Hessian matrix for differentiating the concrete cracks from the background [15]. [PERSON] et al. presented a hybrid method based on the FHT algorithm and Canny edge detector for detecting cracks in concrete structures. The authors also calculated the parameters of the predicted cracks [16]. However, the primary limitation of these classical IPTs is that the techniques pay more attention to extracting local features rather than global properties like cracks on an image, which may downgrade the detection task. So, to improve the crack detection task, researchers started combining classification methods with the classical IPTs. As a consequence, [PERSON] et al. developed a unique approach based on morphological operations and classifier techniques. The morphological operator was used to extract the necessary features, and the classifier algorithms classified real cracks [17]. [PERSON] et al. presented a new framework named Crack-Forest based on Random Structured Forest for detecting cracks as well as characterizing the cracks on concrete roads [18]. [PERSON] et al. presented a hybrid crack detection framework by combining a Canny edge detector for extracting geometric features of the cracks and a supervised ML algorithm named Light Gradient Boosting Machine (LightGBM). The authors evaluated their framework by using photos containing cracks with adverse conditions [19]. In recent years, Deep Learning (DL) models have outperformed classical CV-based techniques for detecting objects. Furthermore, DL models do not need external feature learning; they can perceive features from a significant quantity of input data. Therefore, researchers are paying attention to detecting concrete cracks with more precision using DL algorithms. [PERSON] et al. introduced a Convolutional Neural Network (CNN) classifier and a sliding window technique for detecting cracks in concrete structures. The authors tested the robustness of their model by predicting external images and showed that their model outperformed the [PERSON] and Canny edge detection method [20]. [PERSON] et al. presented a DL model by using a Restricted Boltzmann Machine (RBM) algorithm to detect cracks on bridge structures. The authors trained their model with consumer-grade camera images and used a divergence learning algorithm to obtain optimal parameters [21]. [PERSON] et al. introduced a novel DL framework named NB-CNN by fusing Naive Bayes data with a CNN classifier for extracting cracks on a nuclear power plant from video frames. Their method could maintain the spatiotemporal video coherence and produce better results than LBP-SVM [22]. [PERSON] et al. presented a benchmark road crack dataset for the first time using smartphone images and used a CNN to classify eight types of cracks on road surfaces [23]. [PERSON] et al. focused on both improving crack detection accuracy and reducing the training time of the Deep Learning model.