An end-to-end network port detection method based on deep learning

Abstract

The invention provides an end-to-end network interface detection method based on deep learning. The method comprises the following steps: (1) obtaining switch network interface images of different backgrounds and different cabinets of a machine room; (2) classifying the network interface images of the switch; Marking each image, and making a data label; (3) manufacturing a switch network interfaceimage data set by using a data enhancement technology; And (4) training a deep network YOLO v3 model, and storing a model result. The internet access image is obtained from the camera of the intelligent indoor inspection robot, the position and the category of the internet access in the image are obtained through algorithm processing, and then on-site real-time deployment is achieved. According to the invention, the network interface detection accuracy of the switch in the machine room can be improved, so that the deployment efficiency of the intelligent inspection robot is improved.

Description

technical field [0001] The invention belongs to the field of machine vision, and further relates to an end-to-end network port detection method based on deep learning in the field of machine vision technology. Background technique [0002] At present, indoor environments such as equipment rooms, data rooms, and warehouses all use traditional fixed and discrete detection systems, which cannot fully cover the indoor situation. The frequency of manual inspections can only reach once a week at most. When an emergency occurs , It is impossible to take effective measures to deal with specific parts of the room when necessary, and it is impossible to collect clear information on the scene to the monitoring center at the first time. In serious cases, major social security accidents may also occur. [0003] Although there are some computer room collection robots in the prior art for detection, due to the low degree of intelligence, the final information verification and analysis task...

AI Technical Summary

Problems solved by technology

There are more and more attempts to apply deep learning to target detection. Faster R-CNN is commonly used, such as "Sonar target detection method based on Faster R-CNN" (application number: 201810229078.9, publication number: 108596030 A) The deep learning network Faster R-CNN detects the position of the rectangular frame of each part of the human body, but this model has high false positives when locating and identifying the background, and it is difficult to deal with the complex network port background in the patent scene of the present invention

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