基于改进YOLOv8n的水下目标检测算法  

Underwater target detection algorithm based on improved YOLOv8n

作　　者：李培坤 李锋[1] 葛忠显 张婷[1] Li Peikun;Li Feng;Ge Zhongxian;Zhang Ting(Ocean College,Jiangsu University of Science and Technology,Zhenjiang 212000,China)

机构地区：[1]江苏科技大学海洋学院,镇江212000

出　　处：《电子测量技术》2025年第3期172-179,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(62276117);江苏省自然基金(BK20211341)项目资助。

摘　　要：受水体衰减、散射等因素的影响,水下光学图像存在严重的偏色、模糊等问题,严重降质导致目标分辨率较差,进而不利于开展水下目标检测任务。针对以上问题,为了提高水下目标检测的精度,减少误检和漏检的发生率,提出了一种基于改进YOLOv8n的水下目标检测算法:ESA-YOLOv8。首先该算法在C2f中引入了ESP模块改进Bottleneck结构,ESP模块可以优化网络效率,降低YOLOv8n模型的参数量和计算量;其次,增加一个小目标检测层以提高对水下小目标的检测能力;最后,将轻量级上采样算子CARAFE与注意力机制ECA相继引入颈部网络,提高目标检测精度,实现上采样特征融合的增强。实验结果表明,在水下生物数据集DUO上,本文设计的ESA-YOLOv8算法在模型参数量降低的情况下,mAP@0.5和mAP@0.5:0.95分别高达84.7%和65.5%,较基础模型YOLOv8n分别提升了1.7%和1.8%。高精度的检测结果和模型参数量的降低证明了改进YOLOv8n的有效性和在水下目标检测的应用潜力。Affected by factors such as water attenuation and scattering,underwater optical images suffer from severe color distortion,blurriness,and other issues,resulting in a significant decline in quality and poor target resolution,which in turn is not conducive to carrying out underwater target detection tasks.To address the above problems,in order to improve the accuracy of underwater target detection and reduce the incidence of misdetection and missed detection,this paper proposes an underwater target detection algorithm based on improved YOLOv8n:ESA-YOLOv8.Firstly,the algorithm introduces the ESP module in C2f to improve the Bottleneck structure,the ESP module optimizes network efficiency and reduces the number of model parameters and computations in YOLOv8n;secondly,a small target detection layer is added to improve the detection capability of underwater small targets;finally,the lightweight up-sampling operator CARAFE and the attention mechanism ECA are successively introduced into the neck network to improve the target detection accuracy and realize the enhancement of up-sampling feature fusion.The experimental results show that on the underwater biological dataset DUO,the ESA-YOLOv8 algorithm designed in this paper achieves a mAP@0.5 of 84.7%and a mAP@0.5:0.95 of 65.5%,with a reduction in model parameters.These results represent an improvement of 1.7%and 1.8%,respectively,compared to the base model YOLOv8n.The high accuracy detection results and the reduction in the number of model parameters demonstrate the effectiveness of the improved YOLOv8n and its potential application in underwater target detection.

关 键 词：目标检测 YOLOv8n CARAFE 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN919.8[自动化与计算机技术—计算机科学与技术]