In UAV-based photovoltaic panel defect detection， infrared and visible images provide complementary information on thermal anomalies and surface texture structures. However， local modality misalignment， attenuation of weak responses from tiny defects， and the high computational cost of dual-modal models still limit detection performance and practical deployment. To address these challenges， this study proposes a feature-enhanced lightweight dual-modal detector for UAV-based photovoltaic panel defect detection， termed FLD-DET. Built on an infrared-visible dual-modal detection framework， FLD-DET incorporates a dynamic sparse window （DSWin） module into the backbone feature extraction process. DSWin enhances the retention of weak responses from tiny defects through local convolutional inductive bias， channel compression， and adaptive attention allocation. To mitigate local spatial discrepancies between dual-modal features， a lightweight cross-modal attention （LCoA） mechanism is designed to strengthen the spatial correspondence between infrared and visible features by encoding directional positional information. During the detection feature generation stage， FLD-DET introduces a regional-aware feature aggregation （RAFA） head to enhance the discriminative representation between defect regions and background regions. Experimental results show that FLD-DET achieves a favorable balance between detection performance and inference efficiency on the aerial dual-modal photovoltaic panel defect dataset and demonstrates good recognition capability for tiny defects. FLD-DET also obtains competitive detection results on the M3FD dataset， indicating potential cross-scenario generalization.