Jongtae Lim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jongtae Lim has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Hansung Lee
Department of Computer Education, Gyeongkuk National University (GKNU), Andong, Gyeongsangbuk-do, Republic of Korea
1 shared publication - Mansi Sharma
Graphic Era Deemed to be University, Dehradun, India
1 shared publication
Latest Publications
- Open Access
- 13 June 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (12) , 6004
The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection
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.
- 13 June 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (12) , 6004
Open AccessThe Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection
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.