WDLS: Deep Level Set Learning for Weakly Supervised Aeroengine Defect Segmentation

With the rapid development of the aviation industry, videoscope inspection of aeroengines has become crucial for ensuring aircraft flight safety. Recently, deep learning, particularly convolutional neural networks (CNNs), have shown remarkable efficacy in videoscope inspection tasks. However, these methods usually require large-scale training labels with accurate annotation. In videoscope images, defects are often present at the micrometer level and require manual labeling by professional inspection personnel, posing further challenges to model training. To address these issues, weakly supervised deep level set (WDLS), a fully automatic framework for aeroengine defect segmentation, was proposed in this work. WDLS employs a multibranch structure and an iterative learning strategy to combine a high-performance CNN architecture with level set evolution. First, the similarity-guided region detector was designed to generate class-specific segmentation proposals and initialize the level set function. Second, the adaptive local parameter prediction algorithm was proposed to integrate local priors and constraints into the energy function and optimize the iterative process. Finally, a self-supervised objective function named convexified level set was introduced to represent an improved level set formulation and obtain the final segmentation. The proposed framework is continuously differentiable and unified. Thus, it can be seamlessly embedded in any CNN without postprocessing. Furthermore, the method was evaluated on a new aeroengine dataset Turbo19 and a benchmark dataset. Experimental results demonstrated that the proposed framework meets real-time requirements and achieves better performance than state-of-the-art methods.