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.


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    Title :

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


    Contributors:
    Wang, Chuhan (author) / Chen, Haiyong (author) / Wang, Yining (author) / Zhao, Shenshen (author) / Liu, Kun (author)


    Publication date :

    2025-04-01


    Size :

    5704753 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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