# Lightweight Crack Automatic Detection Algorithm Based on TF-MobileNet
## 2 Related Works
### Crack Identification Using Deep Learning Techniques  
Deep learning is a subfield of machine learning. It not only performs well in classification tasks, but researchers have increasingly found that deep learning also excels in object detection and semantic segmentation, yielding favorable outcomes. In the domain of crack identification, [PERSON] et al. [11] presented a crack detection method based on YOLOv3 for pavement. They manually labeled the collected crack patterns and then trained the model to achieve 88% detection accuracy on their constructed dataset. In another study [12], the authors utilized a Faster R-CNN, which was pre-trained to detect crack images under different shooting conditions, and investigated the effects of weather conditions and light levels on crack detection. They found that crack detection was almost unaffected under clear, cloudy, and foggy conditions, but the precision of identifying cracks was greatly affected under sunset or moonlight. [PERSON] et al. (2019) developed a crack detection model, YOLOv4-FPM, based on an enhanced version of YOLOv4 designed for real-time application. They first optimized the loss function using focal loss to enhance accuracy in identifying cracks against complex backgrounds. Subsequently, they pruned the algorithm, shrinking the network size by 81.8%, which significantly increased detection speed, albeit with some accuracy loss. [PERSON] et al. (2019) refined YOLOv3 for UAV crack data by replacing Leaky ReLU activation functions with Mish, except in the feature fusion network. They introduced a Multi-level Attention Block (MLAB), adding it after the backbone, resulting in a higher mean Average Precision (mAP) on the original database. [PERSON] et al. (2019) designed an automatic sewer pipe defect detection method using an enhanced YOLOv3. They applied an adaptive image scaling strategy and replaced the MSE bounding box loss function with GIoU, achieving an average precision of 92% on their dataset. [PERSON] et al. (2019) proposed a novel pavement crack detection method that integrates deep learning for feature extraction, the Whale Optimization Algorithm (WOA) for feature selection, and Random Forest (RF) for classification, achieving high accuracy in crack detection. [PERSON] et al. (2019) developed an improved Swin-Unet model, incorporating an enhanced skip attention module and residual Swin-Unet (iSwin-Unet) based on the original Swin-Unet. This model effectively performs global modeling of pavement crack features while efficiently exchanging crack feature information. In the field of crack segmentation, [PERSON] et al. (2018) first applied U-Net for crack segmentation. They selected the focal loss function as their evaluation metric and optimized it using Adam. Finally, they used the trained model to detect images with different illumination, cluttered backgrounds, and different crack widths, demonstrating good results and robustness. [PERSON] et al. (2019) put forward a novel approach for road crack detection using a random structure forest algorithm called CrackForest. First, [PERSON] et al. (2019) utilized integral channel features to redefine crack characteristics, aiming to better represent cracks with intensity inhomogeneity. Following this, they introduced the use of a random structure forest as a means of creating a high-performing crack detector capable of identifying cracks of arbitrary complexity. Compared with other crack detection methods, their approach was found to be more efficient and simpler to parallelize. [PERSON] et al. (2020) put a crack segmentation model relying on a Swim Transformer encoder and decoder combined with multilayer perceptual layers. The Crack Transformer (CT) had been proposed for the automated detection of lengthy and intricate cracks in pavements. They trained on a crack image dataset containing different types of environmental disturbances, and the findings indicated that their put CT model possesses the capability to detect fine cracks well. [PERSON] et al. (2021) utilized a DCNN for the segmentation of cracks.