渔业现代化2024，Vol.51Issue(6)：91-99,9.DOI:10.3969/j.issn.1007-9580.2024.06.010
基于改进YOLOv8的轻量级鱼类检测方法
Lightweight fish detection method based on improved YOLOv8
摘要
Abstract
Fish culture is moving toward precision culture,and fish target detection is an important part of precision culture.Fortunately,the use of deep learning holds promise for fish target detection.However,the existing fish target detection models have problems with heavy computation and low accuracy.To address the issues of low accuracy and high computational load in fish target detection,a lightweight fish target detection method based on an improved YOLOv8 model was proposed and named YOLOv8-FCW in this study.Firstly,The experimental comparison of MobileNet,ShuffleNet,GhostNet and C2f-Faster shows that C2f-Faster performs best.Therefore,the FasterBlock from FasterNet was introduced to replace the Bottleneck module in C2f of YOLOv8,reducing redundant computations in the network model.Secondly,the Convolutional Block Attention Module(CBAM)attention mechanism was incorporated to efficiently extract fish body features and enhance the detection accuracy of the network model.Finally,The experimental results show that the loss value and convergence speed of the Wise intersection over union(WIoU)loss function is better than Complete intersection over union(CIoU),Distance intersection over union(DIoU)and Generalized intersection over union(GIoU).Therefore,a dynamic non-monotonic focusing mechanism WIoU was introduced to replace CIoU,accelerating the convergence speed of the network model and improving its detection performance.To verify the detection effect of YOLOv8-FCW on fish,the original model and YOLOv8-FCW were trained and tested on the fish data set.The fish data set consists of 1000 images,which were divided into training set,verification set and test set according to the ratio of 8∶1∶1.Experimental results show that compared with the original model,the improved YOLOv8-FCW model had increased precision by 1.6 percentage points,recall by 5.1 percentage points,and mean average precision(mAP)by 2.4 percentage points,while the weight and computational load were reduced to 80%and 79%of the original model,respectively.YOLOv8-FCW achieves high detection accuracy and efficiency with very small model volume and low computational cost.The model shows high accuracy and robustness.The research can help breeders accurately calculate the number of fish and provide technical references for fish target detection.关键词
图像处理/图像识别/目标检测/YOLOv8Key words
image processing/image recognition/target detection/YOLOv8分类
水产学引用本文复制引用
王鑫怡,刘旭腾,郑纪业,董贯仓,于兆慧,张霞,王兴家..基于改进YOLOv8的轻量级鱼类检测方法[J].渔业现代化,2024,51(6):91-99,9.基金项目
山东省重点研发计划项目"海洋渔业智能装备与大数据平台系统开发及应用(2021TZXD006)" （2021TZXD006）
