# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 4 Experimental Results  
### Secondary Disaster Event Classification Based on CNN-CDMV Model  
The recognition process of the CNN-CDMV kernel for secondary disaster factors is shown in Figure 4. In the figure, (A) is an example of the AlexNet algorithm's result, the process of obtaining the AlexNet model based on the unbalanced division of training set and the performance in the test set, (B) is the recognition of unknown classes by the AlexNet-CDMV model, (C) includes the pictures from unknown classes in the test set, and (D) is the comparison of the mAP between CNN-CDMV and a single CNN algorithm model. The seven CNN algorithms are AlexNet, GoogleNet V2, ResNet-50, VGG-16, EfficientNet-B0, MobileNet V1, and ShuffleNet V2. Figure 4A has the recognition performance advantage model of 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire scene; the secondary disaster factor occupies 40% of the training set of each model; and the proportion of each of the other classes is 15%. After obtaining five CNN models based on the AlexNet algorithm, the 1 sunset model's recognition of the 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire scene's AP is 92%, 80%, 61%, 82%, and 72%, respectively. The 2 welding model's recognition of the 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire scene's AP is 82%, 89%, 62%, 83%, and 70%, respectively. The 3 strong light model's recognition of the 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire scene's AP is 81%, 78%, 94%, 82%, and 59%, respectively. The 4 weak light model's recognition of the 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire scene's AP is 79%, 81%, 65%, 93%, and 75%,  
Figure 4: Decision-making process of CNN-CDMV algorithm. respectively. The 3 fire model's recognition of the 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire scene's AP is 82%, 80%, 66%, 81%, and 89%, respectively. Figure 4B uses AlexNet-CDMV to test an unknown class image, adding the 1 sunset model, 2 welding model, 3 strong light model, 4 weak light model, and 5 fire model obtained in (A) to the CDMV structure. The unknown image was evaluated using CDMV, and each model provided the unknown image's'softmax' results. However, only the probability value of the model's cognitive bias object was provided for the CDMV. That is, the'softmax' layer of 1 sunset model, 2 welding model, 3 strong light model, 4 weak light model, and 5 fire model gives the probability reference values of 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire contained in the unknown image as 1 0.00000577, 20.00003941, 30.01625495, 40.00001936, and 5 0.99998190, respectively. After the calculation of'softmax' in the CDMV, we obtain 10.0000565, 20.00003877, 30.01599148, 40.00019049, and 50.98377361. Where the maximum value 0.98377361 is given by the 5 fire model, the unknown image contained is determined to be a fire. Figure 4D shows a comparison between the CNN-CDMV structure calculation method under seven algorithm models' mAP values. It shows that the mAP obtained under the CNN-CDMV model with AlexNet, GoogleNet V2, ResNet-50, VGG-16, EfficientNet-B0, MobileNet V1, and ShuffleNet V2 is increased by 11.4%, 4.2%, 2.6%, 6.4%, 4.8%, 4.6%, and 9.2%, respectively. Since the CNN-CDMV algorithm is based on a single model, but under the same CNN algorithm, it can improve the accuracy of object recognition, and different CNN algorithms apply to this architecture, which has universal applicability. Among them, ResNet-CDMV based on the ResNet-50 algorithm has the highest fire identification accuracy, and the mAP is 97.6%, which identifies 13 more images than the ResNet-50 algorithm. Among them, five more pictures of welding were identified, the welding recognition AP increased from 93% to 98%, an increase of 5%, more than seven fire images were identified, and the fire recognition AP increased from 88% to 95%, which is an increase of 7%. Table 2 shows the identification of the 13 images based on the CDMV algorithm.