基于 YOLOv8 和 PSP-Ellipse 的火龙果成熟度识别  

Ripeness identification of pitaya fruit based on YOLOv8 and PSP-Ellipse

作　　者：刘昕璞 赵春雷 李志锋 冯超 LIU Xinpu;ZHAO Chunlei;LI Zhifeng;FENG Chao(Qinhuangdao Polytechnic Institute,Qinhuangdao,Hebei 066000,China;North China University of Science and Technology,Tangshan,Hebei 063210,China;Hebei Agricultural University,Baoding,Hebei 071001,China)

机构地区：[1]秦皇岛工业职业技术学院,河北秦皇岛066000 [2]华北理工大学,河北唐山063210 [3]河北农业大学,河北保定071001

出　　处：《食品与机械》2024年第10期122-128,共7页Food and Machinery

基　　金：河北省自然科学基金项目(编号:23HB48608603);河北省教育厅教学科研资助项目(编号:224785043012)。

摘　　要：[目的]提高火龙果成熟度检测准确率及鲁棒性。[方法]采用YOLOv8目标检测模型与PSP-Ellipse分割算法相结合的策略,提出一种高效且准确的火龙果成熟度自动识别方法。先利用YOLOv8的实时目标检测功能对火龙果进行初步定位和识别,再通过PSP-Ellipse的形状识别能力,对火龙果的形状和成熟度进行进一步的精细分类。[结果]火龙果成熟度分类准确率为97.6%,鲁棒性较强。[结论]该方法在复杂背景和多种光照条件下能够显著提高火龙果的自动化分级效率。[Objective]Improve the accuracy and robustness of maturity detection of pitaya fruit.[Methods]Combining the YOLOv8 object detection model with the PSP-Ellipse segmentation algorithm,an efficient and accurate automatic identification method for pitaya fruit maturity was proposed.First,the real-time target detection function of YOLOv8 was used to locate and identify the pitaya fruit initially.Then the shape recognition capability of PSP-Ellipse was used to further fine classify the shape and maturity of the pitaya fruit.[Results]The accuracy rate of maturity classification of pitaya fruit was 97.6%,and the robustness was strong.[Conclusion]This method can significantly improve the automatic classification efficiency of pitaya fruit under complex backgrounds and various lighting conditions.

关 键 词：火龙果 成熟度识别 YOLOv8 PSP-Ellipse 目标检测 形状识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S226.5[自动化与计算机技术—计算机科学与技术]