# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 2 Related Work
### Wavelet Transforms in Deep Learning  
Wavelet transforms are a powerful tool for signal processing, known for their ability to decompose signals into components at various frequency scales. Unlike Fourier transforms, which only capture frequency information, wavelet transforms provide time-frequency localization, making them particularly effective in analyzing non-stationary signals and complex spatial patterns. This property has been exploited in various computer vision tasks, including image denoising, compression, and texture analysis [40].  
In the context of radiographic inspection, researchers have adapted wavelet theory to address challenges in X-ray and \(\gamma\)-ray image analysis. For instance, [PERSON] et al. [41] proposed the Dynamic Weights-Based Wavelet Attention Neural Network (DWWA-Net), which integrated dynamic wavelet transform convolutions (WTConv) and multiview attention to enhance weak defect detection and denoise strong background interference in industrial images. This approach achieved state-of-the-art precision improvements, with a 6.0% increase on the GC10-DET dataset and a 4.3% increase on the NEU dataset.  
Recent advances have focused on wavelet-based network defect detection for industrial applications. For example, [PERSON] et al. [42] proposed a novel CNN-wavelet fusion method that converts laser ultrasonic signals into scalograms for automated subsurface defect width quantification. This method achieved 98.5% validation accuracy and 100% experimental accuracy, demonstrating robust defect characterization without manual feature extraction. However, the small experimental dataset (four signals) and heavy reliance on simulated data raise concerns about the generalizability of this approach to real-world industrial scenarios with complex defect geometries and material variations.  
Another notable contribution is the hybrid method proposed by [PERSON] et al. [43], which integrated wavelet decomposition, Canny edge detection, and a multi-channel CNN to detect surface defects on automobile pipe joints. This method effectively addressed challenges posed by smooth surfaces and processing textures while achieving high classification accuracy under uneven illumination and noise. However, the dependency on manual parameter tuning in pre-processing stages may limit the scalability and applicability of this method in real-world industrial settings.  
Despite significant advancements, 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 becomes particularly evident when detecting defects with diverse morphologies, subtle grayscale variations, and complex edge boundaries. While most existing approaches treat wavelet transforms as auxiliary modules or preprocessing steps, achieving notable results, our work integrates wavelet transforms directly into the YOLO framework. This integration enables end-to-end multi-resolution feature extraction, specifically tailored for radiographic defect detection, allowing the network to dynamically extract multi-scale features and enhance detection accuracy. By doing so, our approach offers a promising direction for advancing automated defect recognition in industrial applications.