Identification method of danger sources at infrastructure construction site based on neural network multi-layer feature information

Due to the complex site conditions of power infrastructure projects and the similar structural features of various danger sources, it is difficult to extract salient features suitable for classification. Although the neural networks with large numbers of available improved loss functions have good application effects in feature recognition, the single depth feature does not make full use of the complementarity between the multiple features of the field environment. In response to these problems, this paper proposes a method for classification and identification of danger sources at infrastructure construction sites based on neural network multi-layer feature information. It completes the image recognition of the infrastructure site by selecting the sparse self-encoding technology, extracts the multi-layer features in the neural network, and then sends the multi-layer features to the softmax classifier to solve the multi-classification problem of dangerous sources, and can extract more suitable based on the analysis of the characteristics of the danger sources on the infrastructure construction site. Through data analysis, the method illustrated in this paper effectively improves the identification rate of the danger sources and shows great significance to ensure the safety of infrastructure construction site.