Dynamic Attention and Context-Aware Feature Fusion for Multi-Scale Solar Panel Defect Detection

Ensuring the accurate detection of surface flaws in PV panels is vital for preserving energy efficiency and minimizing future repair expenses. Nevertheless, the diverse nature of these defects in terms of size, shape, and visibility presents significant localization challenges. Conventional models typically rely on fixed feature hierarchies, uniform spatial weighting, and static fusion strategies. These limitations restrict their ability to capture defects across scales, emphasize relevant regions, and integrate semantic information effectively under visually complex conditions. To overcome these challenges, we propose a Multi-Scale Attention-based Convolutional Neural Network (MSA-CNN), which is a compact detection framework composed of three specialized modules. The multi-scale feature extraction module first captures spatial patterns at varying resolutions through parallel convolutional branches, addressing scale-related limitations. These features are then refined by the dynamic attention module, which adaptively emphasizes defect-relevant regions based on spatial context. Finally, the context-aware fusion module integrates the attention-enhanced features by selectively combining multi-level information, producing semantically consistent representations for accurate detection. Experimental results on the PV Multi-Defect dataset show that MSA-CNN outperforms a range of state-of-the-art methods across all key detection metrics by achieving higher accuracy across all metrics and defect categories, with notable improvements in detecting small, low-contrast, and structurally irregular faults.