## 5 Conclusions  
In this paper, a RASPP module is designed to expand the receptive field of the model so that the region proposals generated by the RPN network contain a broader region of feature information. Moreover, we combine RASPP with top-down and bottom-up pyramid networks to form DPN. DPN can enrich multi-scale feature information, shorten the information flow of the features among different levels, maintain more feature information, facilitate the detection of minor defects, and improve the robustness of the model to defect scales. In addition, we use a double head to perform the classification and regression tasks, respectively. We add a non-local attention mechanism in the double head to enhance defect features and suppress background features, as some defect types are highly similar to the background. Finally, with the proposed DPN-Detector, we obtain an mAP of 80.93% on the steel strip dataset NEU-DET, with 3.52% improvement compared to the baseline. The classification accuracy is 74.64%, and the detection speed reaches 18.62 FPS. The results show that our DPN-Detector has excellent advantages in detecting the defects on the steel strip surface. Our research will contribute to the automatic detection of surface defects in industrial production, promoting the production efficiency.  
However, the proposed method still needs to be further improved. At present, the amount of computation of the method is relatively large and detection speed needs to be further modified. Furthermore, the highly reflected light in the industrial environment will affect the defect detection, which requires the features extracted by the detection method to be more robust. In the future, we will work on developing faster and more robust detection methods for steel strip defects.  
Methodology, X.Z. and M.W.; Supervision, X.Z. and J.L.; Writing--original draft, X.Z. and M.W.; Writing--review and editing, X.Z., M.W., Q.L., Y.F., Y.G., H.L. and J.R. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ROI** & & & & & & & & \\
**Number** & & & & & & & & \\
50 & 77.67\% & 47.57\% & 83.50\% & 90.35\% & 85.93\% & 68.03\% & 90.66\% & 28.42 \\
100 & 78.22\% & 54.86\% & 83.30\% & 90.98\% & 82.82\% & 65.79\% & 91.59\% & 27.00 \\
300 & 79.15\% & 55.51\% & 84.73\% & 91.89\% & 83.80\% & 66.20\% & 92.79\% & 24.55 \\
500 & 80.93\% & 55.33\% & 86.45\% & 92.09\% & 85.03\% & 73.54\% & 93.16\% & 18.62 \\
1000 & 80.97\% & 57.57\% & 84.48\% & 91.27\% & 85.36\% & 73.07\% & 94.08\% & 16.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection results of different ROI numbers.  
**Funding:** This research was funded by Guangdong Basic and Applied Basic Research Foundation, grant number 2020A1515010651.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are openly available at the following URL/DOI: [[http://faculty.neu.edu.cn/songkechen/zh_CN/zhym/263269](http://faculty.neu.edu.cn/songkechen/zh_CN/zhym/263269)]([http://faculty.neu.edu.cn/songkechen/zh_CN/zhym/263269](http://faculty.neu.edu.cn/songkechen/zh_CN/zhym/263269)). (Accessed on 30 December 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.