## 6 Conclusions  
The surface defects of steel were taken as the research object in this paper. A two-stage recognition defect detection framework was proposed in order to solve the problem of complex morphology, small size, and similar features.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**Number** & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**16**} & \\ \hline
**Type** & \multicolumn{4}{c}{**Crazing**} & \multicolumn{4}{c}{**Inclusion**} & \multicolumn{4}{c}{**Patches**} & \multicolumn{4}{c}{**Pitted\_Surface**} & \multicolumn{4}{c}{**Rolded-In\_Scale**} & \multicolumn{4}{c}{**Scratches**} \\ \hline
**Index** & **TP** & **FP** & **P** & **TP** & **FP** & **P** & **TP** & **FP** & **P** & **TP** & **FP** & **P** & **TP** & **FP** & **P** & **Acc** \\ \hline A & 9 & 1 & 0.90 & 4 & 1 & 0.67 & 6 & 0 & 1.00 & 4 & 2 & 0.67 & 8 & 1 & 0.80 & 13 & 3  
The Improved-YOLOv5 model was used to identify the defects on the steel surface. We obtained the suspected defect areas, and the Optimized-Inception-ResnetV2 model was used to perform second-stage recognition of these areas. According to the comparison results of first- and second-stage recognition, the inspection of the entire steel surface was completed.
2. In order to further improve the recognition accuracy of each stage of the model, we made various improvements to the YOLOv5 and Inception-ResnetV2. Through many experiments and tests, we proved that the performance of our model improved significantly.
3. The recognition effect of the two-stage recognition framework in the Enriched-NEU-DET dataset was higher than that of the single-stage model Improved-YOLOv5 by 5.2% (mAP), and the single-stage model Optimized-Inception-ResnetV2 by 13.6% (mAP).
4. In order to verify the detection performance in an actual industrial environment, robots and industrial cameras were used to build real scenes. Based on a comparison, the defect detection performance advantages of the two-stage recognition framework were obvious, which shows the superiority and universality of the model proposed in this paper.
5. From the above research, we see that some types have large differences within categories, and some defects have small gaps between categories. It is easy to cause misjudgments between them during the identification process. Therefore, in future research, corresponding data augmentation and other methods can be carried out on different types of defects to further improve the recognition accuracy.  
Conceptualization, Z.L. and X.T.; methodology, Z.L.; software, Z.L.; validation, Z.L.; formal analysis, Z.L.; investigation, Z.L. and Y.L.; resources, X.T. and X.S.; data curation, Z.L. and Y.L.; writing--original draft preparation, Z.L. and X.L.; writing--review and editing, Z.L. and X.L.; visualization, Z.L.; supervision, X.T.; project administration, X.T.; funding acquisition, X.T. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Taishan Industry Leading Talent Project Special Fund; The National Natural Science Foundation of China (NO.62103234); Major Innovation Project of Shandong Province (NO.2019 JZZY010441); Project ZR2021 QF027 supported by Shandong Provincial Natural Science Foundation.  
Not applicable.  
The authors declare no conflict of interest.