# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 2 Related Work  
### YOLO Architectures in Radiographic Defect Detection  
YOLO is a deep learning-based object detection algorithm that transforms the object detection problem into a regression task, enabling direct prediction of object locations and classes through a single neural network. The overall architecture of YOLO includes convolutional layers, fully connected layers, and an output layer. The convolutional layers extract image features, while the fully connected layers map these features to object positions and classes. The output layer provides the final predictions of object locations and classes. YOLO divides the input image into a grid of cells, with each cell responsible for detecting objects whose center points fall within it. For each cell, YOLO predicts bounding boxes along with their confidence scores and class probabilities. The Non-Maximum Suppression (NMS) algorithm is then applied to remove redundant bounding boxes, retaining those most likely to contain the object. This approach allows YOLO to perform real-time object detection with high accuracy and efficiency.  
Since its inception in 2016, YOLO has evolved through multiple iterations (YOLOv1-YOLOv11) [32, 33], each introducing significant innovations. For example, YOLOv2 introduced anchor boxes to improve localization accuracy [34], while YOLOv3 incorporated feature pyramid networks (FPNs) to enhance multi-scale detection capabilities [35]. Recent advancements have introduced novel components such as the C3k2 and C2 PSA blocks, enhancing YOLO's feature extraction and processing capabilities. These improvements increase computational efficiency without compromising accuracy, while significantly reducing the model's parameter count [33].  
In the field of radiographic defect detection, several studies have adapted YOLO to address the unique challenges posed by welding defects. For instance, [PERSON] et al. [36] introduced a lightweight version of YOLOv5 by simplifying the network structure, reducing the number of parameters, and optimizing computational efficiency. This improved model demonstrated higher detection accuracy and faster inference speeds when applied to X-ray images of welding defects, including porosity, slag inclusion, and lack of fusion. Similarly, [PERSON] et al. [37] enhanced YOLO by incorporating contextual information and a scale-aware mechanism, resulting in the CSA-YOLO model. This model outperformed the traditional YOLO across multiple evaluation metrics, such as precision, recall, and mean average precision (mAP), significantly improving the accuracy of welding defect detection.  
To address challenges related to low contrast and significant variations in defect shapes and sizes, [PERSON] et al. [38] designed a new backbone network, WD-YOLO, specifically for weld defect detection. By replacing the YOLO backbone with the NeXt backbone and adding a dual-attention mechanism, the model enhanced detection performance for targets of varying sizes. Additionally, [PERSON] et al. [39] proposed the S-YOLO model, which introduces full-dimensional dynamic convolution and a NAM attention mechanism to enhance feature representation in regions of interest. This approach effectively addresses challenges such as small target detection, occlusion, and overlap, achieving an 8.9% improvement in mAP compared to the original model.