摘要
Abstract
The surface scratches,abrasions,discoloration,and other defects often occur in the production process of the mobile phone card removal pin,which seriously affect the quality and delivery of products.To address the limitations of the existing card removal pin defect detection methods,this research constructs a card removal pin defect detection model based on YOLOv8 for learning,training,and verifying three common card removal pin surface defects.Through the three steps of dataset construction,model training,and model evaluation,the automatic detection of the surface defects of the mobile phone card removal pin is realized.The experimental results show that the average accuracy of the model in multi-category defect detection reaches 98.45%,and the recognition accuracies of scratches,abrasions,and discoloration defects reach 98.89%,97.87%,and 96.98%,respectively,verifying the effectiveness of the model.In addition,the YOLOv8 model has the advantages of small memory usage and fast detection speed,which significantly improves the engineering applicability of the model.关键词
YOLOv8/手机取卡针/缺陷检测/检测速度Key words
YOLOv8/mobile phone card removal pin/defect detection/detection speed分类
计算机与自动化
