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

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 mAP@0.5.Additionally,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.