# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 2 Related Work
### Vision Based Crack Detection Methods
As a consequence, they developed a Deep Learning model named MobileNetV3-BLS based on MobileNetV3 to detect cracks on concrete surfaces. The authors also claimed that the weights of their method can be updated quickly, which helps to obtain increased accuracy with newly added nodes [24]. [PERSON] et al. proposed a Deep Learning classifier to detect concrete cracks. The authors utilized a genetic algorithm to optimize the parameters of the image processing technique [25]. [PERSON] et al. created a dataset of brickwork masonry facades and built a Deep Learning model using the transfer learning strategy to detect cracks on the masonry facades using their limited data [26]. [PERSON] et al. proposed the You Only Look Once (YOLO) version 2 model for locating cracks with bounding boxes on concrete structures. The model was able to distinguish cracks from handwritten scripts present in the concrete structure [27]. [PERSON] et al. presented a method called Crack Deep Network (CrackDN) based on Faster Region CNN (Fast RCNN) to identify sealed and unsealed cracks having diverse backgrounds in pavement images. The authors extracted features by a Zeiler-Fragus Network (ZF-Net)-based CNN embedded with a sensitivity network in parallel. Finally, they utilized a Region proposal Refinement Network (RPRN) for classifying the cracks [28]. [PERSON] et al. proposed a modified YOLO-V5 network by adding a swin transformer and a bidirectional feature pyramid. The authors claimed that they obtained better performance than the YOLO-V7 model [29]. [PERSON] et al. considered the problem of variable illumination conditions in the case of crack detection in their work [30]. To solve the issue, the authors proposed a model named IlumiCrack, which uses a Gaussian model to play with the brightness of pictures and an SGD model to detect the cracks. However, these models are capable of categorizing and localizing cracks within a concrete structure, but they cannot detect cracks at the individual pixel level. Therefore, among the DL models nowadays, encoder-decoder-based pixel-level crack detection models (i.e., FCN [31], U-net [32]) are becoming more popular for improving the detection accuracy as these models can extract the geometrical shape of the cracks along with localizing them. [PERSON] et al. proposed a novel encoder-decoder-based model called an FCN for detecting cracks where the VGG19 model was used as the downsampler of the proposed FCN. After predicting the crack images, the authors also generated crack skeletons to measure morphological features [33]. [PERSON] et al. proposed an FCN model based on the ResNet-152 encoder network for detecting pavement cracks from black-box camera images. The authors examined their model with transfer learning and without the transfer learning processes. However, Resnet-152 with transfer learning performed better [34]. [PERSON] et al. proposed an FCN model using the VGG16 as an encoder network to detect cracks on concrete surfaces. The authors tested their model on a benchmark dataset and showed that their model obtained a 10.93% and 20.93% improvement over the CNN and SVM model, respectively, with respect to the SA [35]. [PERSON] et al. adopted U-net, which is another encoder-decoder-based network (the improved version of FCN), to segment cracks in concrete structures. The authors introduced the focal loss function for handling the class imbalance problem in their work [36]. [PERSON] et al. also utilized the U-net model with zero paddings in their work for automatically detecting cracks in concrete structures. They trained the model using 200 images collected by an unmanned aerial vehicle and obtained better results than the Canny and Sobel method [37]. However, U-net models can also fall behind in predicting extremely narrow cracks and detecting cracks in adverse conditions. So, nowadays, researchers are continuously integrating different approaches with U-net to address these challenges. [PERSON] et al.