Intelligent identification method for cable position of cableway cable based on residual neural network

Abstract

The invention discloses a residual neural network-based ropeway cable rope position intelligent identification method. The method comprises steps of obtaining abundant ropeway cable rope position images through a manufactured ropeway model, classifying image data, extracting regions of interest of the images, and reserving and labeling the image data with good quality; in the subsequent model training, performing target recognition training on the labeled image data by using the improved residual neural network, and obtaining an optimal neural network model through multiple iterations; and performing neural network training on a test sample by using the obtained optimal model, and finally outputting a cableway cable position result in real time through a Web end. According to the method, the acquired image data is analyzed, the optimal model is trained in combination with the improved residual neural network, and the change of the position of the cable of the cableway can be identified in real time by using the model.

Description

Technical field [0001] The present invention relates to an intelligent identification method, specifically an intelligent identification method based on the residual neural network, belonging to the field of cableway monitoring. Background technique [0002] The cableway is widely used in the scenic spot and the industrial and mining area. The cableway mainly utilizes steel rope traction, and its associated structure is susceptible to atmospheric humidity, steel structure embroidered, air-dried corrosion, etc., resulting in a variation of cable rope due to external force, and may have serious safety incidents. Therefore, the monitoring of the cableway cable is critical. [0003] Traditional monitoring methods are generally carried out through artificial value-focused form. Since the recognition accuracy and accuracy of human eye are very limited, the time will cause visual fatigue that cannot be observed and real-time forecast results. The second is a steel wire rope detection sy...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems existing in the above-mentioned prior art, the present invention provides a method for intelligent identification of cableway cable position based on residual neural network, which does not requi

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