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钢板表面缺陷图像增强与自动标注方法研究

杨璐雅 黄新波 任玉成 韩琪

机械科学与技术2025,Vol.44Issue(3):445-452,8.
机械科学与技术2025,Vol.44Issue(3):445-452,8.DOI:10.13433/j.cnki.1003-8728.20230227

钢板表面缺陷图像增强与自动标注方法研究

Study on Image Enhancement and Automatic Annotation of Steel Plate Surfaced Defect

杨璐雅 1黄新波 2任玉成 3韩琪1

作者信息

  • 1. 西安电子科技大学机电工程学院,西安 710071
  • 2. 西安电子科技大学机电工程学院,西安 710071||西安工程大学电子信息学院,西安 710048
  • 3. 中国重型机械研究院股份公司,西安 710032
  • 折叠

摘要

Abstract

Dataset annotation provides a large amount of labeled data for machine learning.In the dataset production,it needs to draw a box manually for annotation by using the various annotation tools.It is greatly affected by subjective factors.Moreover,due to the complex industrial field environment and unstable image quality,it is difficult to achieve the annotation effect.Therefore,an improved MSR(Multi-scale retinex)steel plate defect dataset enhancement algorithm and an adaptive target box annotation method based on the pixel difference are proposed.Firstly,based on MSR,an adaptive weight calculation method was proposed to automatically determine the weight Wkby calculating the image information entropy without manual adjustment.And the collected defect image was enhanced.Then,it was too much to calculate the pixel difference and extract the target boundary directly for the whole image,so a block calculation method was proposed,and the mean matrix and the second-order difference matrix of each sub block were calculated respectively.By considering the distribution of the target in each sub block,the appropriate sub block was selected to calculate the four boundary of the rectangular box.It assists the defect dataset annotation instead of the manual method.The average IoU is 0.87 and the average detection time is 457 ms,and the average IoU and detection time on the open dataset are 0.84 and 473 ms,respectively.The performance is better than that via the other methods.The detection accuracy of Faster R-CNN and YOLOv5 based on the present algorithm are improved by 4.8%and 5.9%respectively,which can provide datasets with stable quality for the deep learning.

关键词

数据标注/深度学习/数据集增强/像素 2阶差分/自适应目标框标注

Key words

data annotation/deep learning/datasets enhancement/pixel second-order difference/adaptive bounding box annotation

分类

信息技术与安全科学

引用本文复制引用

杨璐雅,黄新波,任玉成,韩琪..钢板表面缺陷图像增强与自动标注方法研究[J].机械科学与技术,2025,44(3):445-452,8.

基金项目

中国重型机械研究院金属挤压与锻造装备技术国家重点实验室开放课题(N-KY-ZX-1104-201911-5881) (N-KY-ZX-1104-201911-5881)

机械科学与技术

OA北大核心

1003-8728

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