# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 1 Introduction
Single-stage methods, such as Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO), directly predict bounding box coordinates and class probabilities, offering faster inference speeds at the cost of slightly lower accuracy. In contrast, two-stage methods, such as Region-based CNNs (R-CNN, Fast R-CNN, and Faster R-CNN), first generate region proposals and then classify and refine these regions, achieving higher accuracy but with increased computational complexity. Among these, YOLO has gained significant attention for its efficiency and real-time performance. Introduced by [PERSON] et al. in 2016 [29], YOLO divides the input image into a grid and predicts bounding boxes and class probabilities for each grid cell. In recent years, YOLO has been widely applied in industrial defect detection due to its efficiency and robustness [30]. However, conventional YOLO architectures rely on standard convolutional operations, which are limited in their ability to capture multi-scale and multi-frequency features inherent in radiographic images. This limitation is particularly pronounced when detecting defects with diverse morphologies, subtle grayscale variations, and complex edge boundaries. To address this, we propose a novel approach that integrates wavelet transforms into the YOLO framework. Wavelet transforms are renowned for their multi-resolution analysis capabilities, enabling the decomposition of signals into distinct frequency subbands [31]. By embedding learnable wavelet kernels into YOLO's backbone network, our method enhances the model's ability to dynamically extract multi-defect, multi-boundary, and multi-grayscale features. In this study, we leverage the multi-resolution capabilities of wavelet transforms within the YOLO framework to achieve effective multi-scale feature extraction, enabling the detection of seven defect categories: cracks, lack of fusion, incomplete penetration, concavity, undercut, slag inclusions, and porosity. Our approach represents a meaningful step forward in automated defect recognition, providing a robust and efficient solution with potential for real-world industrial applications. Specifically, the main contributions of this paper are as follows:  
1. On a dataset composed of 7000 radiographic images, WT-YOLO achieved a 0.0212 increase in mAP75 and a 0.0479 improvement in precision compared to YOLOv11n. The wavelet-enhanced YOLO framework improves multi-scale feature extraction and defect detection accuracy. 2. On a test set containing seven defect types, with 200 images per type, WT-YOLO improved precision by 5.15%, 7.84%, 0.67%, 11.80%, 5.16%, and 2.04% for cracks, lack of fusion, incomplete penetration, concave, undercut, and porosity, respectively. WT-Conv's frequency decomposition effectively suppresses noise, enhancing robustness in complex industrial environments. 3. Compared to manual inspection, WT-YOLO achieved higher precision by 0.37%, 17.47%, 11.29%, and 10.74% for cracks, undercut, slag inclusion, and porosity, respectively, with an inference speed 300 times faster than manual inspection. The comparison between the model's performance and manual inspection results, along with the detection efficiency, provides practical support for the development of hybrid detection systems. Figure 1: Deep learning-based welding defect detection process.