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.

This study addresses the fine-grained segmentation of defects in bridge pavements, which is crucial for the maintenance and structural safety of bridges. Although bridge pavements pose distinctive challenges owing to their unique characteristics and varied defect types, previous studies have primarily focused on the detection of slender cracks. To fill this research gap, we developed a novel end-to-end hybrid method that dynamically combines the vision transformer (ViT) and level set theory to handle the complex geometry of bridge pavement defects. The novelty of the proposed method lies in the configuration of two parallel decoders. These decoders, operating under a unified objective function, share weights and perform simultaneous optimization, thereby facilitating a holistic end-to-end training process. Furthermore, we compiled two new bridge pavement defect datasets, namely BdridgeDefX and BdridgeDef20, which offer broader applicability for practical defect detection. The results of a rigorous experimental validation on four datasets demonstrated the proposed method’s capability of generating accurate defect boundaries and delivering state-of-the-art performance.

Deep supervised learning-based fault diagnosis methods require a large amount of labeled data, which frequently contradicts the typical engineering scenario, in which numerous samples are available but only a small portion are labeled. To conduct a fault diagnosis in this case, unsupervised and semi-supervised representation learning have recently been proposed. However, most of these methods have been designed without much consideration for downstream classification tasks and thus are insufficiently relevant for providing sufficient targeted assistance. Therefore, this study proposes a semi-self-supervised method that consists of two learning stages. In the self-supervised learning stage, two pretext tasks are implemented. Using two auxiliary feature extractors and classifiers, the global and local features of the unlabeled samples can be learned by the feature extractor. In the supervised learning stage, the feature extractor parameters are frozen, and the classifier is trained with the limited number of labeled samples available. The performance of the proposed method was verified using the CWRU open dataset and a self-built experimental dataset. Experiments on these two datasets demonstrated that the proposed method outperformed existing diagnosis methods for a few labeled samples.

In remote sensing image analyses, the extraction of lake water bodies has been emphasized owing to its pivotal role in interpreting aquatic ecosystems, assessing hydrological trends, and detecting environmental changes. Although deep-learning-based techniques have been effectively deployed for this task, several challenges persist, including mis-segmentation of low-contrast regions, insufficient delineation of fuzzy boundaries, and over-segmentation in micro-regions. To address these issues, an innovative, end-to-end segmentation framework is proposed in this study. This framework ingeniously integrates the Geodesic Active Contour (GAC) model with the Vision Transformer (ViT) architecture, thereby offering a robust solution for fain-gained lake water body extraction. Specifically, the conventional GAC model is first revisited and reformulated. Subsequently, the Differentiable Spectral Clustering (DSC) module is designed for automatic contour initialization and lifts the dependence on hard threshold binarization. Finally, an iterative learning strategy is introduced to integrate the aforementioned models within the ViT architecture. This strategy serves to constrain the geometric characteristics, while also permitting dynamic adjustment of evolution parameters, facilitating enhanced refinement of spatial details and accuracy in segmentation results. Comprehensive experiments conducted on the newly constructed Global Lake View (GLV) dataset and a public benchmark demonstrated that the proposed method yields precise lake boundaries and achieves state-of-the-art performance.

With continuous advancements in sensor technology and computer vision, automated surface defect detection has become an important problem in the modern manufacturing industry. Although deep-learning-based defect detection methods have proven successful in various inspection tasks, they often require extensive high-quality pixel-level annotations for model training. Industrial defects, characterized by diverse shapes and intricate textures, require manual labelling by skilled inspectors. This study introduces a weakly supervised defect segmentation framework called the progressively learning dynamic level set (PLDL) to overcome these constraints. This framework incorporates two parallel learning modules and a differentiable level set module interconnected through a progressive learning strategy facilitated by an innovative loss function. Using only image-level labels as inputs, the PLDL framework iteratively refines object boundaries, dynamically optimizes the training process, and generates pixel-level outputs. Our method is efficient and consistent, requiring no manual supervision or post-processing steps. Experimental results on four benchmark datasets representing diverse industrial scenarios reveal that PLDL outperforms recent weakly supervised models. It achieved 76.52%, 85.23%, 85.71%, 84.43%, and 83.17% in mIoU, mPA, Precision, Recall, and F-measure, respectively, indicating its superior effectiveness in weakly supervised defect segmentation.