The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection

Autor: Mansi Sharma, Jongtae Lim, Hansung Lee
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Applied Sciences, Vol 12, Iss 12, p 6004 (2022)
Druh dokumentu: article
ISSN: 12126004
2076-3417
DOI: 10.3390/app12126004
Popis: Steel surface defect detection is challenging because it contains various atypical defects. Many studies have attempted to detect metal surface defects using deep learning and had success in applying deep learning. Despite many previous studies to solve the steel surface defect detection, it remains a difficult problem. To resolve the atypical defects problem, we introduce a hierarchical approach for the classification and detection of defects on the steel surface. The proposed approach has a hierarchical structure of the binary classifier at the first stage and the object detection and semantic segmentation algorithms at the second stage. It shows 98.6% accuracy in scratch and other types of defect classification and 77.12% mean average precision (mAP) in defect detection using the Northeastern University (NEU) surface defect detection dataset. A comparative analysis with the previous studies shows that the proposed approach achieves excellent results on the NEU dataset.
Databáze: Directory of Open Access Journals