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 algo-rithm network consists of a feature extraction module,a feature fusion module,and an image reconstruc-tion module.A hybrid attention mechanism is incorporated to improve the network's ability to extract sig-nificant information from the feature tensor.Additionally,a loss function based on pixel content distribu-tion 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 main-stream 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.
维普
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