To address the challenge of weak spectral intensity differences between dispersed mine targets and the surrounding ground in low light conditions at night, an end-to-end unsupervised visible-polarized image fusion enhancement algorithm is explored. This algorithm uses the polarization characteristics of scattered mines to enhance nighttime mine targets while preserving scene texture details. The fusion algorithm network consists of a feature extraction module, a feature fusion module, and an image reconstruction module. A hybrid attention mechanism is incorporated to improve the network's ability to extract significant information from the feature tensor. Additionally, a loss function based on pixel content distribution is designed to ensure the fused image retains prominent pixel features from the source image, enabling end-to-end network output. For the nighttime landmine scattering dataset, evaluations using seven mainstream image fusion methods showed superior performance across eight metrics, including SSIM and VIF. The fusion-enhanced image in the YOLOv5 model surpassed the intensity image in landmine detection tasks. This model is state-of-the-art and positively impacts subsequent mine detection missions.