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基于YOLOv5的工艺品包装箱缺陷检测

Craft box defect detection based on YOLOv5
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摘要 包装箱是商品流通过程中的重要保障之一,针对跨国运输的外贸工艺品,确保外包装纸箱外观整洁无破损、变形和污渍是极其必要的。文章提出了一种全新的包装箱缺陷检测解决方案,将目标检测技术应用于国内外贸物流仓库;通过在物流仓库进行实际考察拍摄,自制数据集,分别训练YOLOx、YOLOv4、YOLOv5模型。通过检测结果对比得出,YOLOv5为检测效果最准确、效率最高的模型,平均精度均值(Mean Average Precision,mAP)为90.3%,平均精度(Average Precision,AP)值最大,每秒检测帧数(Frames Per Second,FPS)值可满足实际应用需求。 Packaging boxes are one of the important guarantees in the circulation process of goods,for the foreign trade handicrafts transported across borders,it is extremely necessary to ensure that the appearance of the outer packaging box is clean and free of damage,deformation and stains.This article proposes a brand-new packaging box defect detection solution,applying target detection technology to domestic and foreign trade logistics warehouses for the first time.By photographing through actual inspections in logistics warehouses,a dataset is made,and YOLOx,YOLOv4,YOLOv5 models are respectively trained.By comparing the test results,it can be concluded that YOLOv5 is the most accurate and efficient model for detection.Its mean average precision is 90.3%,average precision value is max,the frames per second value can meet actual application needs.
作者 魏佳熠 高成 WEI Jiayi;GAO Cheng(Shenyang University of Technology,Shenyang 111003,China)
机构地区 沈阳工业大学
出处 《无线互联科技》 2024年第12期94-97,共4页 Wireless Internet Technology
关键词 YOLOv5 工艺品包装箱 深度学习 缺陷检测 YOLOv5 crafts packaging box deep learning defect detection
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