Strip Steel Surface Defect Detection Based on Improved YOLOv3 Algorithm

doi:10.3969/j.issn.0372-2112.2020.07.006

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Strip Steel Surface Defect Detection Based on Improved YOLOv3 Algorithm

更新时间：2025-07-08

- Strip Steel Surface Defect Detection Based on Improved YOLOv3 Algorithm
- Acta Electronica Sinica Vol. 48, Issue 7, Pages: 1284-1292(2020)
- 作者机构：
  
  1. 武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北,武汉,430081
  2. 武汉科技大学高温材料与炉衬技术国家地方联合工程研究中心,湖北,武汉,430081
  3. 武汉科技大学理学院,湖北,武汉,430081
  4. 武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北,武汉,430081
  5. 武汉科技大学高温材料与炉衬技术国家地方联合工程研究中心,湖北,武汉,430081
  6. 武汉科技大学理学院,湖北,武汉,430081
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.51774219)
- DOI：10.3969/j.issn.0372-2112.2020.07.006
  CLC： TP391.41
- Published Online：25 July 2020，
  
  Published：2020
- 稿件说明：
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Strip Steel Surface Defect Detection Based on Improved YOLOv3 Algorithm[J]. Acta Electronica Sinica, 2020, 48(7): 1284-1292.
DOI：

Strip Steel Surface Defect Detection Based on Improved YOLOv3 Algorithm[J]. Acta Electronica Sinica, 2020, 48(7): 1284-1292. DOI： 10.3969/j.issn.0372-2112.2020.07.006.

摘要

针对热轧带钢表面缺陷检测中存在的检测速度慢、检测精度低等问题，提出了一种改进的YOLOv3算法模型.使用加权K-means聚类算法来优化确定先验框参数，提高先验框（priors anchor）与特征图层（feature map）的匹配度；同时，调整YOLOv3算法的网络结构，融合浅层特征与深层特征，形成新的大尺度检测图层，提高网络对带钢表面缺陷的检测精度.实验结果表明，改进后的YOLOv3算法在NEU-DET数据集上平均精度均值达到了80%，较原有的YOLOv3算法提高了11%；同时检测速度保持在50fps，优于目前其它深度学习带钢表面缺陷检测算法.

Abstract

To solve the problem of slow speed and low accuracy in the surface defect detection of hot rolled strips

an improved YOLOv3 algorithm is proposed. Firstly

the weighting K-means clustering algorithm is put forward to optimize priors anchor's parameters

which can improve the match between priors anchor and feature map. Secondly

the improved network structure of the YOLOv3 algorithm is proposed to improve the detection accuracy

whose shallow features and deep features are combined to form the new large-scale inspection layer. The experiments are carried out on the NEU-DET dataset

the results show that the average accuracy of the improved YOLOv3 algorithm is 80%

which is 11% higher than that of the original algorithm; the detection speed is 50fps

which is faster than other strip surface defect detection algorithms based on deep learning.

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