Defect detection of bottle preforms is a crucial step in ensuring the quality of PET bottle molding.In order to deploy defect detection models to industrial application scenarios for online detection and improve the accuracy of preform defect detection,a bottle preform defect detection model based on improved YOLOv8,YOLOv8-FEMA model,is proposed.Firstly,embed the FasterNet Block into the C2f module of the YOLOv8 model to reduce the number of model parameters;Then,the EMA mechanism is introduced to make the network more focused on useful feature information and improve the detection accuracy of the model.The experimental results show that compared to the YOLOv8n model,this model reduces the number of parameters and floating-point operations by 27%and 26%,respectively,and improves detection accuracy by 0.03.This model is deployed in bottle embryo defect detection software and can effectively detect bottle preforms defects.
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