A method and system for hazard identification based on deep learning

Abstract

The present invention provides a method and system for identifying hazards based on deep learning, the method comprising: S1, transmitting the image collected by the camera to the background server at a fixed rate; S2, the background server using the deep learning algorithm to collect the image Find the feature, use this feature as the detection condition of the moving image; S3, input the feature into the coefficient dictionary, and obtain the moving target according to the optimal sparse representation; S4, use the binocular vision to perform the motion feature obtained by the sparse representation Positioning to realize hazard identification. The present invention solves the problem that the identification of hazard sources of power transmission and transformation lines is difficult in the prior art, effectively reduces the labor complexity of manual extraction of hazard source features, and its expression of features is more complete and effective than other methods, and finally realizes the identification of hazards. The location of source movement characteristics can protect the safety of power transmission and transformation lines.

Description

technical field [0001] The invention relates to the field of signal and information processing, in particular to a method and system for identifying hazards based on deep learning. Background technique [0002] Deep learning algorithms, derived from artificial neural networks, combine low-level features to form more abstract high-level representation attribute categories or features to discover distributed feature representations of data. Deep learning is a new field in the field of machine learning. Its motivation is to establish and simulate the neural network of the human brain for analysis and learning. It imitates the mechanism of the human brain to interpret data, such as images, sounds and texts. Like machine learning, deep machine learning methods are also divided into supervised learning and unsupervised learning, and the learning models established under different learning frameworks are different. [0003] Nowadays, image recognition using deep learning algorithm...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a hazard identification method and system based on deep learning, which aims to solve the problem of difficult identification of hazards in power transmission and transformation lines in the prior art, and realize the positioning of the motion characteristics of hazards, accurately The distance between the moving hazard source and the target to be protected can be used to protect the safety of the transmission line

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