Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module  

作　　者：Xiwen Zhang Chuanzhong Xuan Zhanfeng Hou 

机构地区：[1]College of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Inner Mongolia,Hohhot 010018,China [2]Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production,Inner Mongolia,Hohhot 010018,China

出　　处：《International Journal of Agricultural and Biological Engineering》2023年第6期207-214,共8页国际农业与生物工程学报（英文）

基　　金：funded by the National Key Research and Development Program of China (Grant No.2022YFF1302300);the Key R&D and Achievement Transformation Plan Project of Inner Mongolia (Grant No.2023YFDZ0006);the Program for Improving the Scientific Research Ability of Youth Teachers of Inner Mongolia Agricultural University (Grant No.BR220128);the Research Program of science and technology at Universities of Inner Mongolia Autonomous Region (Grant No.NJZZ23046).

摘　　要：The non-destructive recognition of coated seeds is crucial for advancing studies in coating theory.Currently,the recognition of coated seeds heavily relies on manual visual inspection and machine vision detection.However,these methods pose challenges such as high misclassification rates,low recognition efficiency,and elevated labor intensity.In response to the aforementioned challenges,this study leveraged deep learning techniques to develop a coated seed recognition model named YOLO-Coated Seeds Recognition(YOLO-CSR),aiming to address the challenges posed by coated seed recognition tasks.The experiment of this study mainly includes the following steps:First,a seed coating machine was set up to coat red clover seeds,resulting in three types of coated red clover seeds.Subsequently,by collecting images of the three types of coated seeds,a coated seed image dataset was further constructed.Then,the YOLOv5s was built,incorporating the Convolutional Block Attention Module(CBAM)into the model’s backbone to enhance its ability to learn features of coated seeds.Finally,the training results of YOLO-CSR were compared with those of other classical recognition models.The experimental results showed that YOLO-CSR achieved the best recognition performance on the self-built coated seed image dataset.The average precision(AP)for recognizing the three types of coated seeds reached 98.43%,97.91%,and 97.26%,with a mean average precision@0.5(mAP@0.5)of 97.87%.Compared to YOLOv5,YOLO-CSR showed a 1.18%improvement in [e-mail],YOLO-CSR has a model size of only 14.9 MB,with an average recognition time(ART)of 10.1 ms and a frame per second(FPS)of 99.Experimental results prove that YOLO-CSR can accurately,efficiently,and rapidly recognize coated red clover seeds.The findings of this study provide technical support for the non-destructive recognition of spherical coated seeds.

关 键 词：coated seed recognition red clover seed YOLO Attention Module CNNS 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S2[自动化与计算机技术—计算机科学与技术]