# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 3 Result and Discussion
### Detection Results of Scaled Networks  
As mentioned in the previous, in MobileNet, the number of channels in each layer of the backbone feature extraction network is adjusted through the channel number adjustment coefficient \(\alpha\) to achieve the purpose of adjusting the amount of parameters. The paper selects different scaling factors \(\alpha\) for different backbone feature extraction networks: when the backbone feature extraction network is MobilenetV1, \(\alpha\) = 0.25, 0.5, 0.75, 1.0; when the backbone feature extraction network is MobilenetV2, \(\alpha\) = 0.5, 0.75, 1.0, 1.3; when the backbone feature extraction network is MobilenetV3, \(\alpha\) = 0.75, 1.0; the model test results with the scaling factor \(\alpha\) = 1 are only shown in Table 2, and the remaining results are shown in Figure 7.  
In general, the number of channels is positively correlated with the feature extraction capability of the backbone and inversely correlated with the amount of computation or number of parameters. The higher the number of channels, the more feature information the network extracts, which in turn increases the detection accuracy, but the increase in the number of parameters results in a certain loss of speed. The effect of different channel number scaling on detection accuracy and speed was shown in Figure 7 and Table 3.  
As can be seen from Table 3, the improved algorithm using network scaling achieved faster detection accuracy compared to the unscaled ones, with MobilenetV1-Yolov4-0.25 achieving the fastest detection speed of 70.26 FPS among all algorithms, and MobilenetV2-Yolov4-1.3 achieving the highest detection accuracy of 93.22%. The effect of different channel number adjustment factors on the test results can be found in Figure 7a. When adjusting the number of channels for MobilenetV1, the average prediction accuracy of the algorithm generally shows an increase trend as \(\alpha\) increases, but the detection speed continues to decrease as shown in Figure 7a; when adjusting for MobilenetV2, the prediction accuracy shows an increasing-decreasing-increasing trend as \(\alpha\) increases, which may be due to the small capacity of the dataset or the inappropriate batch size setting; When adjusting for MobilenetV3, the pattern of the change is basically the same as that for MobilenetV1, i.e., as \(\alpha\) increases, the number of channels increases, the detection accuracy increases, but there is a small loss in detection speed. When compared with the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model-Name** & **Backbone** & **mAP/\%** & **FPS** & **Wear** & **Damage** & **Breakdown** & **Tear** \\ \hline Faster R-CNN [41] & Resnet50 & 91.10 & 3.68 & 0.96 & 0.98 & 0.82 & 0.88 \\ SSD [42] & VGG16 & 94.45 & 30.00 & 1.00 & 1.00 & 0.93 & 0.87 \\ RFBnet [47] & VGG16 & 83.36 & 30.42 & 0.98 & 0.77 & 0.85 & 0.75 \\ M2 det [48] & VGG16 & 92.47 & 24.47 & 1.00 & 0.98 & 0.80 & 0.92 \\ Centernet [49] & Resnet50 & 95.05 & 32.4 & 1.00 & 0.94 & 0.94 & 0.93 \\ Yolov3 [43] & Darknet53 & 88.12 & 28.72 & 1.00 & 0.81 & 0.83 & 0.88 \\ Yolov4 [44] & CSP-Darknet53 & 90.00 & 24.39 & 1.00 & 0.81 & 0.96 & 0.87 \\ \hline  & EfficientNetB0 & 89.12 & 41.91 & 1.00 & 0.81 & 0.82 & 0.94 \\  & EfficientNetB1 & 90.57 & 36.00 & 1.00 & 0.83 & 0.85 & 0.94 \\ EfficientNet-Yolov3 [3] & EfficientNetB2 & 91.38 & 33.53 & 1.00 & 0.85 & 0.85 & 0.95 \\  & EfficientNetB3 & 93.16 & 30.97 & 1.00 & 0.89 & 0.89 & 0.95 \\  & EfficientNetB4 & 97.26 & 26.28 & 1.00 & 0.99 & 0.93 & 0.96 \\ \hline MobilenetV1-Yolov4*(ours) & MobilenetV1 & 89.95 & 51.12 & 0.98 & 0.86 & 0.91 & 0.98 \\ MobilenetV2-Yolov4*(ours) & MobilenetV2 & 88.60 & 41.98 & 0.98 & 0.89 & 0.90 & 0.77 \\ MobilenetV3-Yolov4*(ours) & MobilenetV3 & 93.08 & 44.04 & 0.98 & 0.87 & 0.91 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of various algorithms on the dataset. (*: Channel number scaling factor \(\alpha\) = 1.0). results obtained by Efficientnet-Yolov3 in Ref.