Photovoltaic Defect Image Generator with Boundary Alignment Smoothing Constraint for Domain Shift Mitigation

Abstract: Accurate defect detection of photovoltaic (PV) cells is critical for ensuring quality and efficiency in intelligent PV manufacturing systems. However, the scarcity of rich defect data poses substantial challenges for effective model training. While existing methods have explored generative models to augment datasets, they often suffer from instability, limited diversity, and domain shifts. To address these issues, we propose PDIG, a Photovoltaic Defect Image Generator based on Stable Diffusion (SD). PDIG leverages the strong priors learned from large-scale datasets to enhance generation quality under limited data. Specifically, we introduce a Semantic Concept Embedding (SCE) module that incorporates text-conditioned priors to capture the relational concepts between defect types and their appearances. To further enrich the domain distribution, we design a Lightweight Industrial Style Adaptor (LISA), which injects industrial defect characteristics into the SD model through cross-disentangled attention. At inference, we propose a Text-Image Dual-Space Constraints (TIDSC) module, enforcing the quality of generated images via positional consistency and spatial smoothing alignment. Extensive experiments demonstrate that PDIG achieves superior realism and diversity compared to state-of-the-art methods. Specifically, our approach improves Frechet Inception Distance (FID) by 19.16 points over the second-best method and significantly enhances the performance of downstream defect detection tasks.

Photovoltaic Defect Image Generator with Boundary Alignment Smoothing Constraint for Domain Shift Mitigation

The paper under discussion presents a comprehensive approach to enhancing the quality of defect detection in photovoltaic (PV) manufacturing systems by leveraging a novel image generation framework, the Photovoltaic Defect Image Generator (PDIG). PDIG is designed to address the challenges posed by the scarcity and domain shift of defect datasets inherent in manufacturing environments. The framework is built upon Stable Diffusion (SD), a recent advancement in image synthesis models known for its ability to produce high-quality and diverse outputs by learning from large-scale datasets.

The novelty of PDIG lies in its integration of several key components tailored to the specifics of PV defect detection. First, it introduces a Semantic Concept Embedding (SCE) module, which is aimed at capturing the nuanced relational concepts between defect types and their visual appearances by utilizing text-conditioned priors. This module enhances the generator’s capability to maintain consistency with real-world defect characteristics.

Secondly, the paper details a Lightweight Industrial Style Adaptor (LISA), which addresses the domain distribution shifts by incorporating industrial-specific features into the SD model. LISA employs a cross-disentangled attention mechanism that effectively integrates detailed defect characteristics within the model, thereby augmenting the diversity and realism of generated images. This approach is particularly beneficial in scenarios where the available training data may not fully capture the variability present across different PV production lines.

Another significant contribution is the Text-Image Dual-Space Constraints (TIDSC) module, which refines the generation process by ensuring positional consistency and spatial smoothing. This component enforces alignment between the text and image context during inference, thereby producing defect images with improved localization accuracy. The paper reports that the PDIG framework not only surpasses previous methods in generating high-fidelity defect images but also significantly enhances downstream defect detection tasks. Specifically, PDIG achieves a remarkable improvement in the Frechet Inception Distance (FID) metric by 19.16 points compared to other state-of-the-art approaches, signifying its superior image generation capabilities in terms of realism and fidelity.

The implications of this research are multifold. Practically, it provides a robust toolset for augmenting dataset diversity, which is crucial for training reliable defect detection models for PV manufacturing. Theoretically, the PDIG framework demonstrates the efficacy of integrating domain-specific adaptations into general-purpose diffusion models, paving the way for similar applications in other industrial contexts. Furthermore, the paper hints at future applications in AI by suggesting the potential of PDIG’s components to improve image synthesis tasks in varied domains.

In summary, this paper makes a solid contribution to the field of industrial image generation by proposing a method that effectively bridges the domain shift challenge. The integration of semantic concepts, style adaptation, and dual-space constraints within a diffusion framework not only enriches the dataset but also enhances the performance of defect detection technologies. As the field of artificial intelligence continues to evolve, the modular structure of PDIG could inspire further research into tailored image generation models across different industrial applications.