中国造纸2023，Vol.42Issue(11)：160-168,9.DOI:10.11980/j.issn.0254-508X.2023.11.021
改进YOLOv7的纸张表面缺陷检测研究
Study on Surface Defects of Paper Based on YOLOv7
摘要
Abstract
A one-step paper surface defect detection algorithm with improved YOLOv7 was proposed in this study.The attention mechanism module CBAM was integrated into the backbone and feature extraction network structure to extract information from 2 dimensions including space and channel,to improve the feature extraction accuracy and algorithm stability of small target paper disease.ASPP was added to the backbone network SPP,and could further expand the receptive field,so that the feature information of the smaller target was preserved in the network transmission,which solved the small target information insufficient problem,to improve the performance of paper disease recognition for small targets.Through the homemade paper disease dataset detection experiments,compared with YOLOv7,the precision rate,recall rate and average precision rate mean mAP 0.5 of improved YOLOv7 were improved by 1.5,2.3 and 2.1 percentage points,respectively.关键词
机器视觉/纸张表面缺陷/纸病/YOLOv7/注意力机制Key words
machine vision/paper surface defects/paper defects/YOLOv7/attention mechanism分类
轻工纺织引用本文复制引用
陈钰枚,李兆飞,侯劲,赵俊..改进YOLOv7的纸张表面缺陷检测研究[J].中国造纸,2023,42(11):160-168,9.基金项目
四川省科技计划重点研发项目(2021YFG0055) （2021YFG0055）
企业信息化与物联网测控技术四川省高校重点实验室(2022WZJO1) （2022WZJO1）
四川省自贡市科技局重点科技计划项目(2019YYJC15) （2019YYJC15）
四川轻化工大学科研基金项目(2020RC32). （2020RC32）
