渔业现代化2026，Vol.53Issue(2)：117-127,11.DOI:10.26958/j.cnki.1007-9580.2026.02.012
基于感受野特征的多尺度水下目标检测算法
Multi-scale underwater target detection algorithm based on receptive field features
摘要
Abstract
In order to address the challenges of insufficient ambient lighting,small target size,and target clustering and occlusion leading to decreased detection accuracy in underwater target detection,this study proposes a multi-scale underwater target detection algorithm,FDM-YOLO,based on receptive field features.First,to address the issues of insufficient underwater ambient lighting and the fact that underwater organisms are often small targets with colors similar to their surroundings,the RFCADown module is used to generate large receptive field spatial features,enhancing the extraction of key information about underwater targets.Second,a Dysample upsampling module is introduced to suppress blurring and distortion in traditional upsampling processes.Third,a multi-scale,multi-dimensional information collaboration module,C3K2-IMCA,is designed to improve the representation performance of densely occluded targets.Finally,WIoU is used instead of CIoU loss function to mitigate the negative impact of extreme-shaped bounding boxes on model training for small targets.Experimental results show that FDM-YOLO achieves a 2.1％and 2.0％improvement in mAP50 and mAP@50-95 respectively compared to the benchmark model on the DUO dataset,while the model parameters and computational cost are only 2.35M and 6.0 GFLOPs.The above results verify the efficiency of the improved model in enhancing the detection performance of small underwater targets.关键词
水下目标检测/感受野特征/多尺度/损失函数Key words
underwater object detection/receptive field features/multi-scale/loss function分类
信息技术与安全科学引用本文复制引用
芦新春,王宇,倪立学..基于感受野特征的多尺度水下目标检测算法[J].渔业现代化,2026,53(2):117-127,11.基金项目
江苏省连云港市重点研发计划(CG2426) （CG2426）
