# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 5 Discussion  
Currently, the main methods that can be used for image information recognition are image object detection and classification. The content recognition of images using the two methods is shown in Figure 6. Figure 6A is the recognition of the on-site image of secondary disaster events using the object detection method, (B) is an on-site image of disaster events, and (C) is the recognition of the on-site image of secondary disaster events with the classification method. The disaster events in the image contain four objects: cracks in buildings, trapped persons, flammable objects, and explosive objects. The object detection model in (A) can identify the four objects at the same time, while the image classification model in (C) can only recognize one object class from the image; that is, only one class in the image can be identified, so it requires four classification models to identify all four objects in the image. In this study, the classification method is used to construct a CDMV algorithm through multiple models to classify multiple objects in the image to realize the monitoring and early warning of possible secondary disaster events at the disaster on-site monitoring points. In this study, the principle of CDMV can improve the accuracy of image information recognition as a single CNN model has a cognitive bias toward different objects. Taking the 'VOC2007 + 2012' dataset as an example, there are significant differences in the ability of a CNN model to recognize different objects, among which, for SSD (Backbone is VGG-16 neural network), the AP of 'bottle' and 'plant' was 50.7% and 50.1%, and the AP of 'cat' and 'horse' was 85.8% and 86.9%, respectively [43]. The DSSD (Backbone is ResNet-101 neural network) algorithm has 52.4% and 54.5% AP for 'bottle' and 'plant' and 84.3% and 88.5% AP for 'cat' and 'horse', respectively [44]. The FD-SSD (Backbone is VGG-16) algorithm has 54.1% and 53.9% AP for 'bottle' and 'plant' and 86.8% and 88.0% AP for 'cat' and 'horse', respectively. The same is true for Faster R-CNN (Backbone is ResNet-101) [38], R-FCN (Backbone is ResNet-50) [45], YOLO (Backbone is GoogleNet) [46], YOLOv5 (Backbone is DarkNet-19) [47], DSOD (Backbone is DS/64-192-48-1) [48], DF-SSD (Backbone is DenseNet-S-32-1) [49], etc. In addition, to better identify certain types of features in images, relevant scholars proposed the YOLO hybrid attention mechanism method. Such as NAM-YOLOv7 [50], HAM-YOLOv5 [51], and EfficientNet-YOLOv5 [52], these methods significantly improve the CNN recognition performance of a certain feature in the classes. To improve the recognition accuracy of all classes, feasible mechanisms were also studied based on the characteristics of the CNN itself. In the case of the same proportion of training sets, different models vary in the feature learning ability of a single class, and this limitation of the CNN model exists in the process of learning the features of the whole sample. Due to the large number of weight parameters of the models and their complex compositions, it is  
Figure 6: Image content recognition method based on deep learning. difficult to describe the formation mechanism of cognitive bias [53]. To identify the disaster factors at a disaster site, the recognition accuracy of a single class is demanding. Therefore, it is of great significance to overcome the cognitive bias between different classes of the single CNN model in multiple classification tasks and ensure that the overall recognition accuracy of the model is high so that the recognition accuracy of a single class can achieve the best state and the monitoring and early warning work of disaster events can be carried out accurately. There are two types of image information recognition accuracies that are improved by the CDMV algorithm in this study, for example, the'softmax' results of the ResNet-50 and ResNet-CDMV model are less than 0.5. As shown in Figure 7, the two models identify the scene class contained in image 7 in Table 2. The real scene in the image is fire.