基于网格分割的多尺度酒瓶缺陷检测研究  

Multi-scale Bottle Defect Detection Based on Grid Segmentation

作　　者：吴仕莲 胡涛 汪增福[1] 郑志刚 赵丙坤 祝燕林 杨洋 WU Shiian;HU Tao;WANG Zengfu;ZHEN Zhigang;ZHAO Bingkun;ZHU Yanin;YANG Yang(School of Information Science and Technology,University of Science and Technology of China,Hefei Anhui 230000,China;Luzhou Laojiao Co.,Ltd.,Luzhou Sichuan 646000,China)

机构地区：[1]中国科学技术大学信息科学技术学院,安徽合肥230000 [2]泸州老窖股份有限公司,四川泸州646000

出　　处：《食品与发酵科技》2023年第5期104-108,共5页Food and Fermentation Science & Technology

摘　　要：近年来,深度学习技术在不同计算机视觉任务中都取得了重大突破,基于深度学习的视觉检测方法具有精度高、鲁棒性强的优势。本文针对酒瓶包装缺陷检测领域进行研究,设计了一种基于网格的多尺度缺陷检测模型,通过对原始图像划分网格,将缺陷映射到对应网格位置以解决缺陷形状多样、难以定义的问题;并在不同尺度的特征上检测缺陷,解决酒瓶缺陷尺度变化大的问题。In recent years,deep learning technologies have made significant breakthroughs in various computer vision tasks.Deep learning-based visual inspection methods offer advantages such as high accuracy and strong robustness.This paper focuses on the field of bottle defect detection and presents a research study that designs a multi-scale defect detection model based on grids.By dividing the original image into grids,defects are mapped to their respective grid positions to address the issue of diverse and hard-to-define defect shapes.Additionally,defects are detected on features at different scales,addressing the challenge of varying defect scales in glass bottle defects.

关 键 词：缺陷检测 深度学习 多尺度 全卷积网络 

分 类 号：TS206.1[轻工技术与工程—食品科学]