分析测试学报2025，Vol.44Issue(6)：1024-1033,10.DOI:10.12452/j.fxcsxb.250223113
图像预处理整合策略结合改进YOLOv8模型用于微藻种类识别
An Image Preprocessing Integration Strategy Combined with Improved YOLOv8 Model for Identification of Microalgae Species
摘要
Abstract
To address the limitations of traditional microalgae detection methods,which rely on man-ual microscopy,result in prolonged analysis times,and produce results that are highly susceptible to the technical expertise of personnel,an integrated image preprocessing strategy combined with an enhanced YOLOv8 deep learning model for microalgae identification was proposed.A multi-method integration strategy of Gaussian fuzzy,Laplacian operator and principal component analysis was used to preprocess microalgae images.In the improved model,the SPD-Conv module was incorporated to mitigate the loss of fine-grained information,thereby improving the detection performance for low-res-olution images and small-sized microalgae.A slim-neck architecture was employed to reduce the pa-rameter count and model size,while the SimSPPF was introduced to expedite model convergence and enhance operational efficiency.The experimental results demonstrated that the multi-method integrat-ed preprocessing strategy was able to substantially reduce image noise,and enhance the definition of microalgal contours.Under identical conditions,the improved YOLOv8 model achieved a mean aver-age precision(mAP)of 92.2%,representing a 5.1%improvement over the original YOLOv8 model.Especially,it demonstrated superior performance in detecting small-sized microalgae.In comparison to Faster-RCNN,SSD,RTDETR-l,YOLOv3,YOLOv5,YOLOv6 and YOLOv7 models,the mAP of improved YOLOv8 model increased by 40.2%,6.8%,14.5%,1.2%,5.7%,4.7%and 0.8%,respectively.This method offers valuable insights for advancing microalgae species detection technology.关键词
微藻识别/图像预处理/YOLOv8模型/深度学习Key words
microalgae identification/image preprocessing/YOLOv8 model/deep learning分类
化学化工引用本文复制引用
宁静,钟月妍,刘学英,谢丽霞,王童..图像预处理整合策略结合改进YOLOv8模型用于微藻种类识别[J].分析测试学报,2025,44(6):1024-1033,10.基金项目
湖南省生态环境厅科研项目(HBKT-2021010) （HBKT-2021010）
