Damaged apple detection with a hybrid YOLOv3 algorithm  

作　　者：Meng Zhang Huazhao Liang Zhongju Wang Long Wang Chao Huang Xiong Luo 

机构地区：[1]Schoolof Computer and Communication Engineering,University of Science and Technoogy Bejing,Bejing 1083,China lShunde Innovation School,University of Science and Technology Beijing,Foshan 528399,China

出　　处：《Information Processing in Agriculture》2024年第2期163-171,共9页农业信息处理（英文）

基　　金：National Nature Science and Foundation of China under Grants 62202044 and 62002016;the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515110431;Scientific and Technological Innovation Foundation of Foshan under Grant BK22BF009;the NSFC Youth Scientist Fund under Grant 52007160;the Beijing Natural Science Foundation under Grant L211020;the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)under Grant FRF-IDRY-21-003;the Fundamental Research Funds for the Central Universities and the Youth Teacher International Exchange&Growth Program(No.QNXM20220040).

摘　　要：This paper proposes an improved You Only Look Once(YOLOv3)algorithm for automatically detecting damaged apples to promote the automation of the fruit processing industry.In the proposed method,a clustering method based on Rao-1 algorithm is introduced to optimize anchor box sizes.The clustering method uses the intersection over the union to form the objective function and the most representative anchor boxes are generated for normal and damaged apple detection.To verify the feasibility and effectiveness of the proposed method,real apple images collected from the Internet are employed.Compared with the generic YOLOv3 and Fast Region-based Convolutional Neural Network(Fast R-CNN)algorithms,the proposed method yields the highest mean average precision value for the test dataset.Therefore,it is practical to apply the proposed method for intelligent apple detection and classification tasks.

关 键 词：Rao algorithm Apple detection CLUSTERING Smart agriculture 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]