Xiaoyang Hu, Xinyu Zhao, Keju Zhang, Xin Wang, Jinke Zhao, Xianglong Mu, and Jiaqi Guo
Linear near-infrared imaging, smoke interference, dehazing algo-rithm, MSD-YOLOV5s
To address the limitations of traditional detection methods in complex battlefield environments such as haze and night vision, a simulation and recognition framework optimised for smoke interference is proposed. A mathematical model of atmospheric transmission under hazy night conditions is established, and the imaging advantages of linear-array near-infrared push-scanning systems are analysed. Using the Vega real-time simulation platform, a synthetic battlefield scene is used to simulate smoke effects through particle systems, generating target imaging data under different occlusion scenarios. To improve the accuracy of target recognition, an anti-smoke interference module based on the SDNet dehazing algorithm is integrated before the YOLOV5s detection network. The experimental results show that the proposed MSD-YOLOV5s serial architecture can increase the recognition confidence by 3% under smoke, with a mean average precision (mAP) of 0.908 and a detection speed of 130 FPS. These results demonstrate the robustness and real-time performance of the model in harsh environments, providing a practical solution for infrared detection under smoke interference.
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