Multiscale Complementary Semantic Enhancement Network for Welding Seam X-ray Image Defect Detection

The core challenge in the analysis of welding seam X-ray images is the problem of multiscale defects, where defects can range in size and shape from tiny pores to larger unpenetrated areas. However, the current methods have limitations, such as information loss during feature extraction and inadequate scale adjustment strategies during feature fusion, making it difficult for models to capture complex semantic information and retain key details effectively. In this paper, we propose a Multiscale Complementary Semantic Enhancement Network (MCSEN), which includes a Multi-level Semantic Enhancement (MSE) module and a Multi-level Feature Complementary Aggregation (MFCA) module. The MSE module improves multiscale feature learning through graph convolution-guided feature aggregation, effectively mitigating the problem of information loss and enhancing features' distinguishability and semantic richness. The MFCA module resolves the issues of scale inconsistency and semantic discontinuity in feature fusion by adaptively generating fusion weights and finely adjusting feature allocation. Comprehensive experiments show that MCSEN achieves an overall average precision of 91.9%, significantly higher than other models.