To enhance target detection accuracy in smart grid monitoring， especially under the challenging conditions posed by complex outdoor lighting， a comprehensive framework for fusing polarized and intensity images was proposed. To improve the accuracy of detecting potential hazards in smart grid monitoring systems under complex lighting conditions， this paper proposed a detection method based on the fusion of polarization and light intensity dual-modal information. This framework addressed the inherent difficulties of image analysis in diverse lighting scenarios， ensuring robust and accurate monitoring. Firstly， a dual-path feature fusion network was designed， which used dense convolutional modules to extract features from polarized intensity images and polarization degree images separately， thereby enhancing the retention capability of shallow information. Simultaneously， by constructing feature dependencies in both spatial and channel dimensions， new feature maps were selectively generated， solving the feature aggregation problem in feature fusion. Finally， a multi-scale adaptive structural similarity loss function was introduced， and a weighted algorithm was designed to optimize the quality of reference image generation， enhancing the structural fidelity and target saliency of the fused images， and further improving their quality. Experimental results demonstrate that， compared to state-of-the-art image fusion algorithms， the proposed method shows significant performance improvements across multiple evaluation metrics， compared to intensity images（S₀）. These improvements are not only statistically significant but also visually apparent， as the fused images produced by our method are clearer， more detailed， and more informative. Ablation experiments further validate the effectiveness and practicality of the network modules and loss function. In a custom target detection dataset， the fused images generated by this method achieve a recognition accuracy of 91.5%， with an mAP@0.5 score of 0.916， These results showcase the superior performance of our method in objective evaluations and highlight its significant contribution to enhancing the detection accuracy of subsequent target detection networks in smart grid monitoring.