Spatial Contrast and Semantic Difference Perception Network for Aeroengine Blade Damage Segmentation

Aeroengine blades are critical components in engines, and any minor surface damage may lead to severe consequences. Therefore, high-precision damage segmentation techniques are crucial for accurately identifying blade damage. However, the high-precision localization of blade damages faces challenges due to the loss of detailed spatial features and the confusion of semantic features. In this article, we propose a spatial contrast and semantic difference perception network (SSPN) for aeroengine blade damage segmentation, which effectively enhances the discriminative ability of spatial and semantic feature representations. Specifically, we propose a class-aware spatial contrast (CSC) module to first generate segmentation masks and highlight categories. Then, CSC utilizes a class-aware contrastive learning method to enhance the distinguishability of spatial features. To establish clear semantic boundaries, we design a semantic difference perception (SDP) module that first extracts semantic difference information between feature maps. Then, SDP uses a difference perception loss function to focus on low-quality areas and capture subtle semantic variances, which can produce a more balanced and contrasting semantic feature representation. We conduct comprehensive experiments to demonstrate the superiority of SSPN that achieves 93.42% mIoU on our blade dataset, surpassing existing state-of-the-art methods. Furthermore, we build an aeroengine blade damage analysis system to formulate more rational and economical maintenance strategies that can facilitate the development of the aviation engineering field.