# SFCF-Net: Spatial-Frequency Synergistic Learning for Casting Defect Segmentation of Pre-Service Aircraft Engine Blades in Industrial Radiographic Inspection

## TL;DR

This paper introduces SFCF-Net, a new deep learning method for detecting casting defects in aircraft engine blades using both spatial and frequency-domain features to improve accuracy.

## Contribution

The novel SFCF-Net integrates spatial and frequency-domain features through cross-modal fusion for improved defect segmentation in industrial radiographic inspection.

## Key findings

- SFCF-Net outperforms state-of-the-art methods on the ATBCD-Seg dataset and a public benchmark.
- The proposed modules effectively preserve fine-grained details and improve discrimination of complex defect patterns.
- The method meets practical requirements for automated quality control in blade manufacturing.

## Abstract

Turbine blades serve as critical components in aircraft engines, yet casting defects inevitably arise during manufacturing. Therefore, accurate pre-service turbine blade defect detection is critical for aircraft engine safety. However, existing deep learning-based detection methods face several challenges: poor image quality, intraclass variance, interclass similarity, and irregular defect geometries. Moreover, most existing defect detection methods rely primarily on spatial-domain features, which are insufficient for capturing fine-grained texture information, limiting their ability to discriminate complex defect patterns. To address these challenges, we propose a novel Spatial-Frequency Complementary Fusion Network (SFCF-Net) that synergistically integrates spatial and frequency-domain features through complementary cross-modal fusion for accurate defect segmentation. First, a Selective Cross-modal Calibration (SCC) module is introduced that selectively calibrates spatial-frequency features through gated cross-modal interactions, effectively preserving fine-grained details under poor image conditions. Next, we propose a Cross-modal Refinement and Complementation (CRC) module that employs dual-stage attention mechanisms to model intra- and inter-modal feature dependencies, enabling robust discrimination between similar defect categories while maintaining consistency within the same defect class. Finally, we propose an Asymmetric Window Attention (AWA) module that employs bidirectional rectangular windows for accurate defect geometric characterization. Comprehensive experiments on the Aero-engine Turbine Blade Casting Defect Segmentation (ATBCD-Seg) dataset and a public benchmark demonstrate that SFCF-Net consistently outperforms state-of-the-art methods across multiple evaluation metrics, meeting practical requirements for automated quality control in blade manufacturing.