Deep Learning for Hot Rolled Steel Surface Rust Defects Detection

Wai Yang Chen, Kein Huat Chua, Mohammad Babrdel Bonab, Kuew Wai Chew, Stella Morris, Li Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Currently, the conventional approach for the hot rolled steel defect inspections and decisions are made manually based on the standard guideline. However, the quality of the assessments is not consistent due to human error. Therefore, it is essential to develop an automatic inspection system that can decide whether to hold or release the rusted hot rolled steel based on its severity. Artificial intelligence has recently become one of the most frequently cited topics in the development of product defects inspection systems. This project aims to develop a deep learning-based hot rolled steels rust detection system to assist decision-making. The detection processes can be divided into hot rolled steel detection, rust detection, and the decision-making process. Object detection and color detection techniques are adopted in the model for hot rolled steel detection and rust detection respectively. In this study, there are three types of deep learning object detection framework were tested which are Single Shot Detector (SSD) MobileNetv1, SSD MobileNetv2 and Faster RCNN. As Single Shot Detector (SSD) MobileNetv2 has the optimum performance in terms of accuracy and inference speed, it was chosen as the deep learning architecture for the object detection while Hue Saturation Value (HSV) color model is used for the color detection. The hold/release decision-making output is based on the percentage of the detected rusty area from the image. Based on the simulation results, the accuracy of the rust defect detection for hold and release are 96.05% and 97.92%, respectively.

Original languageEnglish
Title of host publication2022 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781665487030
DOIs
Publication statusPublished - 2022
Event12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022 - Virtual, Online, Malaysia
Duration: 2022 May 212022 May 22

Publication series

Name2022 12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022

Conference

Conference12th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2022
Country/TerritoryMalaysia
CityVirtual, Online
Period22-05-2122-05-22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Instrumentation
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Deep Learning for Hot Rolled Steel Surface Rust Defects Detection'. Together they form a unique fingerprint.

Cite this