## 6 Discussion  
As can be seen from Table 3, traditional detection algorithms can achieve high detection accuracy with fewer images, but their real-time performance is poor. These algorithms include the HSVM-MC [15], HCGA [11], and CAE-SGAN [47]. For deep learning detection algorithms (such as M-Pooling CNN [17], Improved YOLO [19]), their detection accuracy increases with data volume and their real-time performance is better than that of the traditional algorithms. In this paper and references, \"average detection time\" refers to the sum of the time from data input to their models to get results. This time does not include the delay in image acquisition and data transmission between systems. This time is only a theoretical detection time. In the actual production line, the actual time is slightly larger than this value due to data transfer between different task modules. Our network's average detection time for a single image was 0.0183 s, and it can detect about 54 images per second. Considering that the speed of a steel rolling production line is 10-30 m/s and the view field of a single camera is 50-100 cm, it requires a detection speed of detection system to be 10-60 FPS. Compared with the M-pooling CNN algorithm and improved YOLO algorithm, our algorithm used the least amount of data. Its real-time performance was weaker than those two algorithms, but the detection speed of our network was 54.6 FPS, which means it meets the basic requirements of online real-time detection. To further verify the performances of our algorithm in this paper, we tested and compared various algorithms on the Northeastern University (NEU) surface defect database. As can be seen from Table 4, a very deep network is not really required for the online real-time defect classification task; the detection accuracy, speed and complexity of the network need to be balanced. \begin{table}
\begin{tabular}{c c|c|c|c} \hline \hline
**Defects** & **Targets** & **Precision** & **Recall** & **f1-Score** & **Samples** \\ \hline \multicolumn{5}{c}{Improved VGG19 Network} \\ \hline Cracks & 0.9836 & 1.0000 & 0.9917 & 60 \\ \hline Inclusion & 0.9344 & 0.9500 & 0.9421 & 60 \\ \hline Scab & 1.0000 & 1.0000 & 1.0000 & 60 \\ \hline Pitted Surface & 1.0000 & 0.9833 & 0.9916 & 60 \\ \hline Rolled in Scale & 1.0000 & 1.0000 & 1.0000 & 60 \\ \hline Surface Scratch & 0.9500 & 0.9500 & 0.9500 & 60 \\ \hline Surface Seams & 1.0000 & 0.9000 & 0.9474 & 10 \\ \hline Weighted Avg & 0.9786 & 0.9784 & 0.9784 & 370 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Task** & **Accuracy** & **Average Detection per Image** & **Samples** \\ \hline M-Pooling CNN [17] & DNN & 93.03\% & \begin{tabular}{c} 0.0062 s \\ 161.3 FPS \\ \end{tabular} & 2927 \\ \hline HCGA [11] & Traditional & 95.04\% & \begin{tabular}{c} 0.158 s \\ 6.3 FPS \\ \end{tabular} & 351 \\ \hline HSVM-MC [15] & Traditional Classification & 95.18\% & \begin{tabular}{c} 1.1044 s \\ 0.9 FPS \\ \end{tabular} & 900 \\ \hline Improved YOLO [19] & DNN & 97.55\% & \
\begin{tabular}{c} 0.012 s \\ 83.3 FPS \\ \end{tabular} & 4655 \\ [PERSON] et al., 2018 & Classification and location & 98.20\% & unknown & 10,800 \\ \hline Ours & DNN & 97.75\% &
\begin{tabular}{c} 0.0183 s \\ 54.6 FPS \\ \end{tabular} & 1850 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between traditional algorithms and DNN algorithms. Although the model in Author's work [48] achieved an accuracy of 98.1% and a recognition speed of 476.2 FPS (2.1 ms), a NVIDIA GeForce GTX 1080 Ti was used to train and test the neural network. Although the model is simple in structure and easy to build, the original data were pre-processed by the normalization Z-Score function. It is not an end-to-end model, and the time to pre-process the original data was much greater than the time for the network to recognize defects.