Aero-Engine Borescope Image Defect Detection Algorithm Using Symmetric Feature Extraction and State Space Model

Enhancing the effectiveness of aviation engine borescope inspection is critical for flight safety. Statistics indicate that engine defects contribute to 20% of mechanical-related flight accidents, while existing defect detection and segmentation models for borescope images suffer from a low operational efficiency and suboptimal accuracy. To address these challenges, this study proposes a Visual State Space with Multi-directional Feature Fusion Mamba (VMmamba) model and constructs a real-world borescope defect dataset. First, a feature compensation module with symmetrical diagonal feature optimization fusion is developed to enhance the feature representation capabilities, expand the receptive fields, and improve the feature extraction of the model. Second, a content-aware upsampling module is introduced to restructure contextual information for complex scene understanding. Finally, the learning process is optimized by integrating Smooth L1 Loss with Focal Loss to strengthen defect recognition. The experimental results demonstrate that VMmamba achieves a 43.4% detection mAP and 36.4% segmentation mAP on our dataset, outperforming state-of-the-art models by 2.3% and 1.4%, respectively, while maintaining a 29.2 FPS inference speed. This framework provides an efficient and accurate solution for borescope defect analysis, offering significant practical value for aviation maintenance and safety-critical decision making.