To overcome the challenges of diverse defect morphologies，low contrast，and large-scale variations in the electroluminescence(EL)images of monocrystalline silicon solar cells，this paper presents an improved RT-DETR-based defect detection algorithm，namely GAE-DETR. First，a GDSANet backbone module is designed that adaptively focuses on salient defect regions through a dynamic spatial attention mechanism and incorporates a frequency-domain-enhanced feed-forward network to capture abnormal changes in the periodic grid structure of EL images. Second， an ESA-PGE lightweight Transformer encoder is proposed，which uses partial channel self-attention and dynamic sparse gating mechanisms to reduce computational complexity while preserving global context modeling capability. Furthermore，a DHAF dual-level attention feature fusion module is constructed to achieve fine-grained integration of cross-scale features. Experimental results on a self-built monocrystalline silicon solar cell EL image dataset show that GAE-DETR achieves 93. 6% mAP@50 and 71. 2% mAP@50-95， which are 5. 2 and 8. 9 percentage points higher than those of the baseline model， respectively. Meanwhile，the number of parameters is reduced by 17. 4%，and the computational complexity(FLOPs) is reduced by 19. 2%，achieving dual optimization of detection accuracy and computational efficiency.