多光谱融合的红外舰船目标轻量化检测

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关键词：红外舰船；多光谱目标检测；轻量化；模型剪枝；知识蒸馏中图分类号：TN291；TP391 文献标识码：Adoi：10.37188/OPE.20253308.1327 CSTR：32169.14.OPE.20253308.1327

Abstract： In order to solve the problems of large size，low eficiency，and high deployment requirements for embedded devices of infrared multi-spectral ship targe detection models，a lightweight ship targe infrared multi-spectral detection model YOLOv8n-MFLW was proposed. Firstly，the model replaced the backbone network with a lightweight network，HGNetv2. Based on GSConv convolution，the modules of HGBlock and C2f were reconstructed to reduce the model parameter count while retaining the model's feature extraction and fusion capabilities. A self-adaptive sparse structured pruning algorithm，La-Depgraph，was proposed to prune the model，leading to a significant reduction in the model's parameters. Finally，an intermediate feature knowledge distilation learning strategy was employed to recover the accuracy loss caused by pruning and enhance the model's detection performance.Experimental results show that compared to the original model，the improved lightweight ship targe infrared multi-spectral fusion detection model achieves a detection accuracy of 96.4% ，an increase of 1.2% . The model's parameter count，computational complexity，and memory usage are only 0.9MB ，3.5 GFlops，and 2.3MB ，respectively，re duced by 88.1% ， 81.2% ，and 82.8% . Therefore，the proposed model is small in size and high in accuracy，it has a better detection performance and is capable for ship target detection tasks in complex environments.

Key words： infrared ships；multispectral object detection；lightweight；model Pruning；knowledge distillation

1引言

在现代化海战中，红外成像制导技术因其具备全天候工作能力、强大的穿透力、远距离作用、反隐身、伪装识别和抗干扰能力等多项优点而得到广泛应用。（剩余13059字）