# Adaptive Attention-Enhanced Yolo for Wall Crack Detection
## 2 Related Work  
### Wall Crack Detection  
To detect concrete cracks, researchers have developed a variety of Image Processing Technology (IPT) [12] methods that can detect cracks on many different objects, such as masonry [13], bridges [14], roads [15], and walls [16]. These techniques mainly rely on basic detection methods such as thresholding and histogram analysis to identify the presence, width, and direction of cracks [17]. However, in practice, problems such as interference from environmental noise make edge detection difficult. To overcome these problems, researchers have introduced various computer vision techniques, such as denoising processing and edge augmentation techniques. [PERSON] et al. [18] introduced an image enhancement algorithm designed specifically for pavement damage detection, which solves the challenge of uneven illumination by calculating a correction factor to normalize the background illumination variations, and then accurately separates the image features from the background using the beamlet transform. [PERSON] et al. [19] proposed a threshold detection method which uses the Canny operator and a triple thresholding system for processing infrared images and combines it with K-means clustering to generate binary images of crack locations for robust and automated visual detection. While these visual methods have advanced the accuracy of crack detection, they remain constrained by varying environmental conditions. In addition, some crack detection techniques [20] that combine extended finite element methods with genetic algorithms,although effective in specific contexts, have certain limitations in practical applications, resulting in insufficient accuracy of the recognition results.  
Gradually, the use of ML [21] in the field of crack detection has become more widespread, and researchers have developed a variety of related detection techniques [22]. These techniques typically utilize IPT to extract features and subsequently classify these features to determine cracks. Since IPT cannot accurately extract all features in some cases, there may be some uncertainty in the final detection results. Nevertheless, this class of methods has an excellent performance when dealing with large-scale crack data. In this area, support vector machines are heavily used due to their effectiveness, as they are able to accurately locate crack areas by analyzing features in concrete images [23]. Principal component analysis is often used in conjunction with SVMs due to its means of dimensionality reduction.  
Recently, deep learning (DL) has been rapidly adopted for wall crack detection, yielding impressive results due to its superior image processing capabilities. Researchers have developed various methods based on it to enhance the recognition and localization of cracks, even in complex and highly noisy images. [PERSON] et al. [24] employed a deep convolutional neural network architecture to detect and recognize cracks, avoiding the complex process of manually calculating defect features. [PERSON] et al. [25] introduced CrackFormer, an innovative network for fine-grained crack detection that uses a SegNet-like encoder-decoder architecture and introduces a novel attention module that captures long-range contextual information while sharpening semantic features. [PERSON] et al. [26] introduced CTNet, a model for road crack detection based on a Transformer architecture. The method leverages the Transformer's ability to capture global and long-range dependencies and incorporates a multi-scale local feature augmentation structure. [PERSON] et al. [27] proposed YOLOv2 to recognize the cracks in bridge image locations, dividing the cracks in each image into multiple regions. Similarly, this method faces challenges such as difficulties in processing large amounts of data.