# Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network
## 3 Results and Discussion
### Visualization of Features  
As the feature extractor of the YOLO_v2, the deep CNN network was a key component. In this paper, the features extracted by'resnet18' were visualized to explain the influence of the feature extraction process on the detection results. The feature images of L1-L8 (Figure 5) were displayed respectively. Figure 6 is the feature images of L1 and L2, and 64  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Detection Precision (\(AP\))** & **Computational Cost (s)** \\ \hline L1 & 0.18 & 2411 \\ L2 & 0.40 & 2558 \\ L3 & 0.82 & 2637 \\ L4 & 0.80 & 2767 \\ L5 & 0.86 & 2944 \\ L6 & 0.89 & 3299 \\ L7 & 0.87 & 3739 \\ L8 & 0 & 4155 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detection results of different feature extraction layers (’resnet18’).  
Figure 5: The architecture of the ’resnet18’.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Size** & **Detection Precision (\(AP\))** & **FPS** \\ \hline
32 \(\times\) 32 & 0 & 20 \\
64 \(\times\) 64 & 0.15 & 17 \\
128 \(\times\) 128 & 0.81 & 14 \\
256 \(\times\) 256 & 0.88 & 13 \\
512 \(\times\) 512 & 0.88 & 11 \\
1024 \(\times\) 1024 & 0.89 & 10 \\
2048 \(\times\) 2048 & 0.88 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection results of different sizes of testing images.  
feature images were obtained for each layer. Some feature images showed the outline of the crack. But there was interference by other non-target objects (some grass and background).  
Figure 7 is the feature images of L3 and L4, and 128 feature images were obtained for each layer. Compared with L1 and L2, the resolution of the feature images was reduced due to the convolution process. Some extra interference was filtered out. The ideal feature (crack shape, marked by a red circle) of the crack was extracted from some feature images. Therefore, using L3 and L4 as a feature extraction layer, the detection effect (Table 4) was better than L1 and L2.  
Figure 6: Feature images of L1 and L2. (**a**) Raw image; (**b**) Feature images of L1 (64 images); (**c**) Feature images of L2 (64 images).  
Figure 7: Feature images of L3 and L4. (**a**) Feature images of L3 (128 images); (**b**) Feature images of L4 (128 images).  
Figure 8 is the feature images of L5 and L6, and 256 feature images were obtained for each layer. The resolution continued to decrease and the features were still visible, as shown in the marked location in Figure 8. Using L5 and L6 as feature extraction layers, the detection performance (Table 4) remained at a high level.  
Figure 9 is the feature images of L7 and L8, and 512 feature images were obtained for each layer. At this point, the resolution continued to decrease. At L7, the crack features were still visible, but at L8, the image became extremely fuzzy and the visual features disappeared. This was also confirmed in Table 4 (detection results).  
Figure 8: Feature images of L5 and L6. (**a**) Feature images of L5 (256 images); (**b**) Feature images of L6 (256 images).  
Figure 9: Feature images of L7 and L8. (**a**) Feature images of L7 (512 images); (**b**) Feature images of L8 (512 images).  
In general, a deeper network layer (deeper layer to get lower resolution feature image) could help to filter out some non-target objects (grass and background). However, it did not mean that the lower the resolution, the better the detection results. If the resolution was too low, it was easy to ignore the features of the object (crack), which can be confirmed in Table 4. Therefore, the selection of the feature extraction layer was worth further studying, which determined the feature information obtained by the YOLO_v2 detection layer. Figure 10 shows some results of crack feature extraction ('resnet18' with 'L6' feature extraction layer).