Aiming at the problem of poor detection performance of military objects at night under low-light and smoke occlusion，MLEC-YOLO model is proposed to be dedicated to the detection of military objects at night.Firstly，an enhanced low-frequency feature extraction network is constructed as the backbone network，by combining the multi-scale low-frequency feature extraction module and the dynamic feature fusion module，the multi-scale low-frequency feature extraction and key features perception are carried out respectively.Secondly，a deep path aggregation network is designed in the neck network to enhance the feature representation from the backbone network.Finally，four decoupling heads with different resolutions are adopted to accommodate military targets of different sizes in night scenes.Simulation results on the self-built dataset Nighttime_Military and the public dataset BDD100K show that the proposed scheme significantly outperforms most of current mainstream object detection models in terms of detection accuracy and generalization ability.