## 6 Conclusions  
Steel surface defect detection is a very difficult task, due to various atypical defects. In this study, we propose a hierarchical method for detecting various atypical defects on steel surfaces. We divided the steel surface defect detection into object detection problems and object segmentation problems according to the type of defect. We defined the detection of pitted surface, inclusion, patches, rolled-in scale, and crazing as an object detection problem because these kinds of defects have characteristics of objects, such as closed boundary, different appearance and uniqueness. Scratch detection is defined as an image segmentation problem, since it has a very thin and elongated shape. In order to apply different defect detection methods according to the defect types, the proposed approach hierarchically combines a classifier with object recognition and an image segmentation algorithm. The proposed model classifies defect images as scratch images or other defect images in the first step. In the second step, object detection or image segmentation algorithms are applied according to the classification result of the first step. The proposed method is able to detect steel surface defects with high accuracy by applying different detection algorithms that are suitable for each defect type. To summarize the experimental and verification results, the proposed approach achieves 98.6% accuracy in scratch and other types of defect classification and 77.12% mAP in defect detection using the NEU dataset. In future studies, we will conduct a study on scratch detection for simultaneously detecting both strong and weak scratches.  
Conceptualization, M.S., J.L. and H.L.; methodology, M.S. and H.L.; software, M.S.; validation, M.S., J.L. and H.L.; formal analysis, H.L.; investigation, M.S. and H.L.; resources, J.L.; data curation, M.S. and H.L.; writing--original draft preparation, M.S. and H.L.; writing--review and editing, M.S., J.L. and H.L.; visualization, M.S.; supervision, J.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the MIST (Ministry of Science, ICT), Korea, under the National Program for Excellence in SW, supervised by the IITP (Institute of Information and communications Technology Planning and Evaluation) in 2022 (2019-0-01113).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
NEU Northeastern University  
mAP Mean average precision  
AI Artificial intelligence  
HOG Histogram of oriented gradient  
LBP Local binary pattern  
EDDN End-to-end defect detection network  
SSD Single shot multibox detector  
CNN Convolutional neural network  
VGG Visual geometry group  
DDN Defect detection network  
ResNet Residual neural network  
Faster-RCNN Region-based convolutional neural network  
YOLO You Only Look Once  
FCN Fully convolutional network  
IoU Intersection over union  
PFF Pyramid feature fusion  
GCA Global context attention  
DAN Dual attention network  
TAS-Net Triple attention semantic segmentation network  
TLU-Net Transfer learning-based UNet  
GT Groundtruth  
XGBoost Extreme gradient boosting  
ML Machine learning  
FPN Feature pyramid network  
ReLU Rectified linear unit  
DSSD Deconvolutional single shot detector  
CM Confusion matrix  
AP Average precision  
SVM Support vector machine  
NNC Neighbor classifier  
mIoU MeanIoU MeanIoU