南京邮电大学学报（自然科学版）2026，Vol.46Issue(3)：70-79,10.DOI:10.14132/j.cnki.1673-5439.2026.03.008
CCE-YOLO:一种基于改进YOLO11n的水下目标检测算法
CCE-YOLO:an underwater target detection algorithm based on improved YOLO11n
摘要
Abstract
Underwater target detection holds significant application value in fields like marine resource exploration and ecological monitoring.However,in complex underwater environments,challenges such as frequent target occlusion,severe light attenuation,and insufficient multi-scale target representation are prevalent.To address these challenges,this study proposes an improved underwater target detection model named CCE-YOLO,based on YOLO11n.First,a cascade group attention module(C2CGA)is in-troduced to enhance the original C2PSA by improving the PSA mechanism.This module strengthens cross-level feature interaction capabilities through group attention and cascaded information transmis-sion,thereby improving the model's discriminative performance for occluded and weak-texture targets.Second,a partial multi-scale feature aggregation module(CSP-PMSFA)is designed,utilizing progres-sive channel splitting and multi-scale convolution fusion strategies to enhance feature representation for multi-scale underwater targets.Finally,an efficient upsampling module based on shifted channel mixing(EUCB-SC)is constructed to strengthen spatial-channel interactions and mitigate the parameter redun-dancy and artifact issues associated with traditional upsampling.Experimental results on the DUO dataset show that CCE-YOLO achieves 85.8%in mAP@0.5 and 67.2%in mAP@0.5:0.95,representing improve-ments of 1.4 and 1.3 percentage points over the baseline model,respectively.Meanwhile,its generaliza-tion and robustness are verified on the RUOD dataset,demonstrating effectiveness in complex underwa-ter environments.关键词
水下目标检测/YOLO11n/级联分组注意力/多尺度特征聚合/高效上采样Key words
underwater target detection/YOLO11n/cascaded grouped attention/multi-scale feature ag-gregation/efficient upsampling分类
信息技术与安全科学引用本文复制引用
赵雪峰,郭勇杰,狄恒西,仲兆满,仲晓敏..CCE-YOLO:一种基于改进YOLO11n的水下目标检测算法[J].南京邮电大学学报（自然科学版）,2026,46(3):70-79,10.基金项目
国家自然科学基金(41004003)、江苏省海洋科技创新项目(JSZRHYKJ202201)、江苏省水利科技项目资助(2020058)、连云港市第"521工程"科研立项项目(LYG06521202131)和连云港市重点研发计划(产业前瞻与关键技术)(CG2527)资助项目 （41004003）
