# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 5 Discussion
The'softmax' probability reference values of a single ResNet-50 output for 1 _s_unset, 2 _w_elding, 3 \(s\) strong light, 4 _w_eak light, and 5 \(s\) fire scenarios are 1 0.533388, 2 0.001140279, 3 0.00000185, 4 0.000195662, and 5 0.46527588, respectively. The value of sunset is the largest; so, the ResNet-50 model considers the scene contained in image 7 to be sunset. The 5 \(s\) fire model in ResNet-CDMV gives the'softmax' probability reference value of the 5 \(s\) fire in image 7 as 0.47225126, and only this value is output. Moreover, 1 0.516423, 2 0.00204462, 3 0.001644969, and 4 0.00763621 are the'softmax' probability reference values of 1 _s_unset, 2 _welding, 3 \(s\) strong light, and 4 _w_eak light, respectively, and do not output. The 1 _s_unset model, 2 _w_elding model, 3 \(s\) strong light model, and 4 _w_eak light model only output'softmax' probability reference values for 1 _s_unset, 2 _welding, 3 \(s\) strong light, and 4 _w_eak light recognition, which are 1 0.31958973, 2 0.002117465, 3 0.029371442, and 4 0.0022207, respectively. They are normalized together with the'softmax' probability reference value of the fire at the CDMV decision level to obtain 1 0.38712312, 2 0.00256491, 1 0.03557802, 4 0.00268996, and 5 0.57204399. The normalized'softmax' probability reference value of the fire is the largest. Therefore, the ResNet-CDMV model confirms that image 7 is the correct judgment of the 5 \(s\) fire, and images 1, 3, and 12 in Table 2 show the same situation. The probability reference values of output'softmax' after ResNet-50 and ResNet-CDMV model identification are all less than 0.5, as shown in image 2 in Table 2; the real scene in this image is welding. The ResNet-50 model'softmax' result values of 1 _s_unset, 2 _w_elding, 3 \(s\) strong light, 4 _w_eak light, and 5 \(s\) fire are 0.012486734, 0.32064572, 0.06004316, 0.19397007, and 0.41285425, respectively, in which the value of fire is the  
Figure 7: Representative samples and their recognition processes. largest; therefore, the ResNet-50 model misidentifies the welding scene as the fire scene. The ResNet-CDMV model'softmax' result values of 1 sunset, 2 welding, 3 strong light, 4 weak light, and 5 fire are 0.027238317, 0.66015106, 0.27432773, 0.3358008, and 0.35803366, respectively, in which the welding value is the largest. The ResNet-CDMV model correctly identifies the information contained in the image, and the same situation is shown in images 4, 5, 6, 8, 9, 10, and 11 in Table 2.  
The ResNet-50 algorithm can only understand a single piece of information contained in the image, and the CDMV method proposed in this study simultaneously integrates multiple ResNet-50 models of different purposes to realize the recognition of multiple objects in an image. From the perspective of algorithm efficiency, compared with the multi-target monitoring algorithm, there are two disadvantages and one advantage. The first disadvantage is that the position of the disaster factor in the picture cannot be marked directly. The second disadvantage is that the mAP result of the ResNet-CDMV model is obtained through the calculation of 10 ResNet-50 models, and the time cost is 10 times that of a single ResNet-50 model. The advantage of this algorithm is that it overcomes the cognitive bias of a single model to multiple objects and has a higher mAP. The location of the detection object in the field environment is determined, and the algorithm only needs to give early warning to the types of secondary disasters existing in the scene information. Additionally, based on the importance of secondary disaster events, the significance of the recognition accuracy is greater than the time consumed. Therefore, the shortcomings of the CDMV method will not have a negative impact on the early warning effect. In addition, since the emergency handling of secondary disasters involves the allocation of emergency resources, there are strict requirements for high-precision identification and early warning.