A security risk plan search assistant decision algorithm using deep neural network combined with two-stage similarity calculation

Abstract

In view of the nonlinearity and uncertainty of safety accident risk assessment, firstly, based on the deep neural network, the training criterion of the network is changed, and the triplet convolutional neural network with the similarity measure as the cost function is proposed. The inactive multi-scale set features are extracted from them, so that the semantic features obtained by learning are suitable for security risk image retrieval. In the image retrieval application, the training samples of the retrieved data set are not enough to train a large network, and the innovative application of migration learning to security risk image retrieval proposes to train the network with data sets similar to the retrieved data sets. Then based on the traditional nearest neighbor algorithm, this paper proposes a case similarity calculation method based on two-dimensional structure of structural similarity and attribute similarity, input the characteristic attribute value of the current emergency event, and conduct similar case retrieval. The final calculation returns the historical case and its solution that the user is most similar to the currently entered incident feature. The experiment proves that the maximum relative error between the output of the network and the expected output value is 5.17%, and the minimum relative error is 1.38%, which has high accuracy.

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“…However, in term of accuracy, the algorithm has not yet reached the mainstream visual odometer accuracy. Reference [25] et al used the convolutional neural network to learn the optimal feature representation of image data for visual odometer estimation and demonstrated the robustness of its algorithm in dealing with image motion blur and illumination changes. However, the experimental results also show the dependence of the proposed algorithm on training data, especially when the frame speed of the image sequence is too fast, the algorithm error is large.…”

“…To verify the performance of the target detection method based on the improved YOLOv3, the proposed method is compared with the methods in [24], [25], and [27] in terms of detection accuracy and detection speed. Among them, the accuracy ratio, recall rate, harmonic mean and processing speed (frame/s) of evaluation index selection of detection accuracy are calculated as follows:…”

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