农业机械学报2024，Vol.55Issue(11)：132-139,8.DOI:10.6041/j.issn.1000-1298.2024.11.014
基于改进YOLO v7的鲑鱼检测模型轻量化研究
Lightweight Salmon Detection Model Based on Improved YOLO v7
摘要
Abstract
In order to achieve rapid and accurate identification of salmon in complex underwater environments,a lightweight salmon detection model,YOLO v7-CSMRep,was proposed based on YOLO v7.Firstly,by adopting the Stem module,the first four convolutional operations in the backbone layer were merged into an efficient convolutional operation,reducing the computational load of the model.Secondly,the ELAN and ELAN-H modules of the YOLO v7 network were replaced with the multi-directional reparameterization(MRep)module,which enhanced the one-way feature extraction capability while greatly reducing parameters and calculations.Finally,at the end of the backbone layer,the convolutional block attention module(CBAM)was integrated to enhance the network's spatial and channel feature extraction capabilities.The experimental results showed that the improved model's volume,parameter count,and computational load were reduced by 4.28％,5.29％and 31.30％,respectively.The F1 score and mAP0.5 were increased by 0.5 and 0.7 percentage points,and reached 93.1％and 97.1％,respectively.Additionally,the frame rate was increased by 15.41％,and reached 140.8 f/s.Compared with that of YOLO v5s,YOLO v6s,YOLO v7,YOLO v7-tiny,and YOLO v8s models,the mAP0.5 was improved by 1.0,2.0,0.7,0.8,and 1.2 percentage points,respectively.Therefore,the method proposed can rapidly and accurately identify salmon and provide technical support for biomass monitoring in deep-sea aquaculture.关键词
深远海养殖/鲑鱼检测/YOLO v7/Stem模块/多尺度重参数化/卷积块注意力模块Key words
deep-sea aquaculture/salmon detection/YOLO v7/Stem module/multi-directional reparameterization/convolutional block attention module分类
农业科技引用本文复制引用
郑荣才,谭鼎文,徐青,陈大勇,元轲新..基于改进YOLO v7的鲑鱼检测模型轻量化研究[J].农业机械学报,2024,55(11):132-139,8.基金项目
国家重点研发计划项目(2022YFD2401201)、广东省海洋经济发展(海洋六大产业)专项资金项目(GDNRC[2023]33)、南方海洋科学与工程广东省实验室(湛江)项目(011Z23002)和湛江湾实验室人才团队引进科研项目(ZJW-2023-05) （2022YFD2401201）
