DAFSF: A Defect-Aware Fine Segmentation Framework Based on Hybrid Encoder and Adaptive Optimization for Image Analysis

Accurate image segmentation is a fundamental requirement for fine-grained image analysis, providing critical support for applications such as medical diagnostics, remote sensing, and industrial fault detection. However, in complex industrial environments, conventional deep learning-based methods often struggle with noisy backgrounds, blurred boundaries, and highly imbalanced class distributions, which make fine-grained fault localization particularly challenging. To address these issues, this paper proposes a Defect-Aware Fine Segmentation Framework (DAFSF) that integrates three complementary components. First, a multi-scale hybrid encoder combines convolutional neural networks for capturing local texture details with Transformer-based modules for modeling global contextual dependencies. Second, a boundary-aware refinement module explicitly learns edge features to improve segmentation accuracy in damaged or ambiguous fault regions. Third, a defect-aware adaptive loss function jointly considers boundary weighting, hard-sample reweighting, and class balance, which enables the model to focus on challenging pixels while alleviating class imbalance. The proposed framework is evaluated on public benchmarks including Aeroscapes, Magnetic Tile Defect, and MVTec AD. The proposed DAFSF achieves mF1 scores of 85.3%, 85.9%, and 87.2%, and pixel accuracy (PA) of 91.5%, 91.8%, and 92.0% on the respective datasets. These findings highlight the effectiveness of the proposed framework for advancing fine-grained fault localization in industrial applications.