农业机械学报2025，Vol.56Issue(1)：388-396,409,10.DOI:10.6041/j.issn.1000-1298.2025.01.037
基于UMS-YOLO v7的面向样本不均衡的水下生物多尺度目标检测方法
Multi-scale Object Detection Method for Underwater Organisms under Unbalanced Samples Based on UMS-YOLO v7
摘要
Abstract
In response to the challenges posed by significant variations in biological scales and the issue of sample imbalance in underwater object detection,a multi-scale object detection method for underwater organisms(UMS-YOLO v7)was proposed.Firstly,a feature extraction module was designed,comprising switchable atrous convolutions.This module captured multi-scale target features across various receptive field sizes,ensuring a more comprehensive extraction of feature information.Secondly,a lightweight universal upsampling operator was employed to fuse contextual information,enhancing the model's ability to learn features for objects.Finally,by combining two similarity metrics,Wise-IoU and normalized Wasserstein distance,the localization accuracy of targets at different scales was improved,simultaneously mitigated the impact of uneven distribution of multi-scale samples on the model.The experimental results demonstrated that the proposed model significantly enhanced detection accuracy compared with other current models,with average accuracies of 64.5％and 68.9％on the RUOD and DUO datasets,respectively.Compared with the YOLO v7 model,UMS-YOLO v7 improved multi-scale object detection accuracy,and precise detection of underwater organisms can also be achieved in complex underwater environments.On the DUO dataset,the average accuracy for large,medium,and small-scale objects was respectively increased by 8.3 percentage points,4.8 percentage points,and 12.5 percentage points,respectively,with the most notable improvement observed for small objects.In comparison with other existing models,the improved model exhibited higher detection accuracy,and it was better suited for underwater biological multi-scale object detection tasks.Additionally,it exhibited generalization,robustness,and adaptability for samples with different data distributions.关键词
水下生物/多尺度目标检测/YOLO v7/空洞卷积/上采样算子/相似性度量Key words
underwater organisms/multi-scale object detection/YOLO v7/atrous convolution/upsampling operator/similarity metrics分类
信息技术与安全科学引用本文复制引用
张明华,黄基萍,宋巍,肖启华,赵丹枫..基于UMS-YOLO v7的面向样本不均衡的水下生物多尺度目标检测方法[J].农业机械学报,2025,56(1):388-396,409,10.基金项目
国家自然科学基金项目(61972240、42106190) （61972240、42106190）
