摘要
[目的/意义]对虾病害严重危害对虾养殖业。针对对虾病害发病快、死亡率高等特点,高密度的工厂化养殖等模式需要一种高效率对虾发病检测方法替代传统人工检查方法,实现对虾发病的及时预警。[方法]提出一种基于改进YOLOv8(You Only Look Once)和多元特征的对虾发病检测方法。首先利用改进YOLOv8网络从对虾夜间水面红外图像中进行前景提取,再利用Farneback光流法和灰度共生矩阵(Gray Level Co-occurrence Matrix,GLCM)提取对虾视频片段的运动特征与图像纹理特征,利用提取到的特征参数构建训练数据集,训练支持向量机(Support Vector Machine,SVM)作为分类器用于检测对虾视频片段,实现对正常与发病的对虾视频片段的检测分类。[结果和讨论]训练好的SVM分类器在300个测试样本上的表现为检测准确率平均值为83%,检测效果达到设计要求。检测误差主要是将发病片段错误地检测为正常片段。该误差主要受水面对虾数量和视频影响。[结论]本研究实现了对对虾发病的检测,提供了一种基于计算机视觉的检测方法。但受条件限制,仅在工厂化养殖环境下进行了实验,尚不能适用于多种养殖环境,仍有改进空间。
[Objective]In recent years,there has been a steady increase in the occurrence and fatality rates of shrimp diseases,causing substantial impacts in shrimp aquaculture.These diseases are marked by their swift onset,high infectivity,complex control requirements,and elevated mortality rates.With the continuous growth of shrimp factory farming,traditional manual detection approaches are no longer able to keep pace with the current requirements.Hence,there is an urgent necessity for an automated solution to identify shrimp diseases.The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy.The improved YOLOv8(You Only Look Once)network and multiple features were employed to detect shrimp diseases.[Methods]To address the issue of surface foam interference,the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image.This target detection approach accurately recognizes objects of interest in the image,determining their category and location,with extraction results surpassing those of threshold segmentation.Taking into account the cost limitations of platform computing power in practical production settings,the network was optimized by reducing parameters and computations,thereby improving detection speed and deployment efficiency.Additionally,the Farnberck optical flow method and gray level co-occurrence matrix(GLCM)were employed to capture the movement and image texture features of shrimp video clips.A dataset was created using these extracted multiple feature parameters,and a Support Vector Machine(SVM)classifier was trained to categorize the multiple feature parameters in video clips,facilitating the detection of shrimp health.[Results and Discussions]The improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops.According to the results of the ablation experiment,replacing the backb
作者
许瑞峰
王瑶华
丁文勇
於俊琦
闫茂仓
陈琛
XU Ruifeng;WANG Yaohua;DING Wenyong;YU Junqi;YAN Maocang;CHEN Chen(Zhejiang Mariculture Research Institute,Wenzhou 325000,China;Shanghai Ocean University,Shanghai 201306,China;Zhejiang Key Lab of Exploitation and Preservation of Coastal Bio-Resource,Wenzhou 325000,China;Wenzhou Key Laboratory of Marine Genetics and Breeding,Wenzhou 325000,China)
出处
《智慧农业（中英文）》
CSCD
2024年第2期62-71,共10页
Smart Agriculture
基金
浙江省重点研发计划(2021C02025)
温州市重大科技攻关项目(ZN2021001)
浙江省“三农九方”科技协作计划(2023SNJF077)
国家重点研发计划课题(2020YFD0900801)。
