深空探测学报（中英文）2025，Vol.12Issue(2)：179-189,11.DOI:10.15982/j.issn.2096-9287.2025.20250003
改进的YOLOv8n轻量化火星表面岩石检测算法
Lightweight Mars Surface Rock Detection Algorithm Based on Improved YOLOv8n
摘要
Abstract
Due to the demand for safe obstacle avoidance in the autonomous navigation of Mars rover in complex terrain and the double constraints of computational resources and energy supply of the onboard platform,a lightweight detection model,YOLOv8-LMD,was constructed,aiming at realizing the requirements of high precision and lightweight characteristics of the rock detection algorithm on the surface of Mars.First,the lightweight backbone network was reconstructed based on the HGNetv2 architecture to realize the preliminary compression of model parameters.Secondly,a multi-scale feature fusion network structure was designed,and the neck network was reconstructed by integrating slim-neck and ASF-YOLO to effectively improve the feature characterization of rock targets at different scales.In addition,a lightweight detection head was designed by using the convolutional sharing strategy,which reduced the computational complexity and enhanced the classification and localization accuracy at the same time.Finally,a pruning algorithm was used to prune the model parameter redundancy to further compress the model,and the knowledge distillation technique was used to achieve the compensation and optimization of the accuracy.Through experiments,it is found that compared with YOLOv8n,YOLOv8-LMD accuracy was improved by 1.7%,the computational amount was reduced by 68%,the parameter amount was reduced by 77%,and the model size was reduced by 75%.Therefore,it can be concluded that the model proposed in this paper is more suitable for the task of rock detection on the surface of Mars.关键词
YOLOv8n/火星表面检测/轻量化/通道剪枝/知识蒸馏Key words
YOLOv8n/Mars surface detection/light weighting/channel pruning/knowledge distillation分类
信息技术与安全科学引用本文复制引用
戴娟,刘经纬,苏中,朱翠..改进的YOLOv8n轻量化火星表面岩石检测算法[J].深空探测学报（中英文）,2025,12(2):179-189,11.基金项目
国家自然科学基金(61703040,61603047) （61703040,61603047）
