无线电工程2025，Vol.55Issue(2)：264-270,7.DOI:10.3969/j.issn.1003-3106.2025.02.005
基于轻量化网络的水下目标检测算法
Underwater Target Detection Algorithm Based on Lightweight Network
摘要
Abstract
Unmanned Underwater Vehicle(UUV)based on machine vision frequently encounters the problems of limited computing resources of embedded devices and slow real-time detection speed during operation.To solve these problems,a lightweight network detection algorithm,namely YOLOv8-FasterECA-Slim-neck-Focaler-EIoU(YOLOv8-FESF),is designed.In the backbone network,a novel C2f_Faster_ECA module is established based on the FasterNet Block and the Efficient Channel Attention(ECA)mechanism to reduce the number of parameters and computational load of the feature network.Moreover,the Slim-neck is employed as the neck structure to further compress the model scale.The detection head is reengineered to leverage the concept of parameter sharing to merge the feature extraction modules,thereby reducing the model's weight and improving the detection speed.The frame regression loss function Focaler-EIoU is utilized to dynamically adjust the loss value to resolve the problem of imbalanced sample difficulty and improve the detection accuracy.The experimental results show that the proposed model performs well on the RUOD dataset.Compared with the YOLOv8n baseline model,it witnesses a 40%reduction in parameters and a 54%decrease in computation,a 10.5 frame/s increase in detection speed,and only a 0.1%drop in mean Average Precision(mAP),rendering it suitable for deployment on underwater target detection platforms with constrained computing device resources.关键词
Focaler-EIoU/YOLOv8/水下目标检测/轻量化网络/PConvKey words
Focaler-EIoU/YOLOv8/underwater target detection/lightweight network/PConv分类
信息技术与安全科学引用本文复制引用
许朝龙,解志斌,宋科宁..基于轻量化网络的水下目标检测算法[J].无线电工程,2025,55(2):264-270,7.基金项目
国家自然科学基金(62276117) （62276117）
高端外国专家引进计划(G2023014110)National Natural Science Foundation of China(62276117) （G2023014110）
Program for High-end Foreign Experts Introduction(G2023014110) （G2023014110）
