# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 1 Introduction  
Welding technology [1, 2, 3] is a metallurgical process used to join materials, creating welds with high strength, excellent sealing performance, and material continuity. This method ensures the structural integrity and lifecycle stability of products, making it indispensable across various industries. Compared to alternative joining techniques (e.g., riveting or adhesive bonding), welding is more material-efficient, reducing the need for additional components and lowering production costs. Its precision, reliability, and cost-effectiveness have led to widespread adoption in industries such as aerospace, defense, shipbuilding, chemical engineering, machinery, automotive manufacturing, and household appliances [4]. In these sectors, welding plays a critical role in producing durable and reliable joints essential for both high-performance and everyday applications. However, due to the rapid cooling of the weld metal from a high-temperature liquid to a solid state within a short time, the welding process is prone to defects, resulting in non-uniform microstructures in the weld. Factors such as insufficient welding current,groove contamination, unstable welding conditions, and foreign materials further exacerbate the formation of defects, including cracks, lack of fusion, incomplete penetration, concavity, undercut, slag inclusions, and porosity. These defects compromise the mechanical properties of the weld area, making it a critical vulnerability in industrial products [5]. To address these challenges, Non-Destructive Testing (NDT) techniques have been developed and widely implemented to detect and evaluate defects without damaging the material. Traditional NDT methods, including Ultrasonic Testing (UT), Radiographic Testing (RT), Magnetic particle Testing (MT), Penetrant Testing (PT), and Eddy current Testing (ET) have significantly improved the quality and efficiency of industrial products [6; 7; 8; 9]. Recent advancements have introduced more sophisticated techniques, such as Time-of-Flight Diffraction (TOFD) ultrasonic testing [10], Phased Array Ultrasonic Testing (PAUT) [11], Computed Radiography (CR) [12], Digital Radiography (DR) [13], Acoustic Emission testing (AE) [14], and ultrasonic guided-wave testing [15]. Among these, RT stands out for its ability to penetrate materials using X-ray or \(\gamma\)-ray, enabling the visualization of internal structures and the detection of defects through radiation absorption and scattering. RT is widely favored for its broad applicability, strong generalizability, clear and intuitive results, ease of long-term storage, and high detection rates. Despite its advantages, traditional RT film interpretation remains subjective, labor-intensive, and inefficient [16; 17; 18]. The manual evaluation of RT films is prone to human error and inconsistency, highlighting the need for automated defect recognition methods to enhance efficiency, standardization, and intelligence in defect detection [19; 20; 21]. Recent advancements in artificial intelligence (AI), particularly deep learning, have revolutionized the field of automated defect recognition. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have demonstrated remarkable success in tasks such as image recognition, natural language processing, and medical diagnosis [22; 23; 24; 25]. In the context of RT, deep learning models can automatically learn hierarchical features from raw data, eliminating the need for manual feature engineering and significantly improving accuracy and efficiency [26]. Unlike traditional methods that rely on explicit feature extraction, CNNs adopt an \"end-to-end\" approach, directly mapping input images to defect detection and classifications [27]. This capability has led to the development of various AI-based methods for weld seam image recognition. The deep learning-based welding defect detection process is shown in Figure 1.  
In radiographic defect detection [28], methods are broadly categorized into single-stage and two-stage approaches.