## 4 Experiments
### The Result of Defect Detection  
We perform extensive experiments on the NEU-DET dataset to evaluate the performance of DPN-Detector and compare it with RetinaNet [32], Cascade R-CNN [40], DETR [41] and YOLO-V5. (1) The \(F1\) is used to evaluate the classification performance of different detectors for defect categories. DPN-Detector has excellent classification ability due to the complementary fusion of the two detection heads. As shown in Table 2, DPN-Detector has the highest classification performance of 74.64%, significantly outperforming all one-stage detectors in the classification of all defect categories. Moreover, it shows improvement compared to the two-stage detectors. On the whole, DPN-Detector has better classification performance for steel strip defects. Furthermore, better classification performance contributes to better defect detection, which is demonstrated in the following experimentsIn addition, the performance of two-stage detectors is generally better than that of one-stage detectors. Additionally, high detection accuracy is extremely important for strip quality assurance, that is why we chose a two-stage detector as the baseline. The detection results of DPN-Detector for each defect type shows that DPN-Detector has better robustness than other detection methods with the same evaluation metric. (3) The PR curves are used to evaluate the detection performance of DPN-Detector for each defect type. As shown in Figure 7, the area enclosed by each curve and the coordinate axis is the AP value of each defect. It can be seen obviously that the detection performance of Cr and RS is worse than that of other defects. The reason lies in the high similarity of the two types of defects to the background, and their complex and varied shapes. Furthermore, the two types of defects are very scattered. Other types of defects have a strong contrast to the background, and appear in the form of lumps, so the detection effect is relatively good. (4) We use a single GPU to perform inference on the model with a total of 540 test images. The FPS of each detector is shown in Table 3. The FPS of DPN-Detector reaches 18.62, which can meet the needs of practical industrial applications. The one-stage object detector YOLO-V5 is famous for its fast detection speed with a maximum 54.00 FPS, but it does not perform well in the detection results of defects. Compared with Cascade R-CNN, the addition of DPN module shows good cost performance in terms of balancing the detection performance and speed of Faster R-CNN. Although DPN-Detector is not the fastest in de \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Cr** & **In** & **Pa** & **PS** & **RS** & **Sc** & **Overall** \\ \hline RetinaNet [32] & 48.22\% & 73.33\% & 84.61\% & 73.46\% & 55.63\% & 71.13\% & 67.73\% \\ YOLO-V5 & 49.70\% & 75.49\% & 84.44\% & 75.98\% & 61.20\% & 85.61\% & 72.07\% \\ Faster R-CNN [29] & 51.90\% & 72.55\% & 86.02\% & 74.34\% & 62.70\% & 83.97\% & 71.91\% \\ Faster R-CNN+FPN [11] & 50.95\% & 77.01\% & 84.79\% & 74.33\% & 63.08\% & 86.52\% & 72.78\% \\ Cascade R-CNN [40] & 51.94\% & 80.70\% & 85.04\% & 74.63\% & 60.85\% & 87.97\% & 73.52\% \\ DPN-Detector & 52.25\% & 80.85\% & 88.37\% & 76.51\% & 61.50\% & 88.34\% & 74.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The F1 comparison of different detectors.