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Tool abnormal placement detection method and system based on double-index metric learning

A technology for metric learning and detection methods, applied in the field of image processing, can solve the problems of error-prone cost, low degree of automation, low efficiency, etc., and achieve the effect of avoiding time-consuming problems

Pending Publication Date: 2020-10-30
SUN YAT SEN UNIV
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Problems solved by technology

[0003] In order to solve the above technical problems, the object of the present invention is to provide a method and system for abnormal tool placement detection based on dual-index metric learning, which can solve the problems of low efficiency, low degree of automation, and error-prone existing in the existing tool abnormal placement detection method and high cost

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  • Tool abnormal placement detection method and system based on double-index metric learning
  • Tool abnormal placement detection method and system based on double-index metric learning
  • Tool abnormal placement detection method and system based on double-index metric learning

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Embodiment Construction

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, it is only set for the convenience of illustration and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art sexual adjustment.

[0037] The present invention improves the existing metric learning network structure in the field of deep learning—relational network structure, and uses the improved model as a tool abnormal placement detection model. The model uses the lightweight MobileNetV2 network structure as the feature extraction network to improve the speed of model training and testing; it uses a three-layer convolutional neural network to automatically learn the similarity between two pictures. Based on the existing relationship network mo...

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Abstract

The invention discloses a tool abnormal placement detection method and system based on double-index metric learning, and the method comprises the steps: obtaining a tool box picture of a correct placement tool, marking the tool box picture, and obtaining marking information; according to the marking information, cutting tools in the tool box pictures into correct tool pictures independently, carrying out data expansion on the correct tool pictures, and obtaining a training set and a verification set; training and verifying a preset tool exception placement detection model based on double-indexmetric learning through the training set and the verification set, and adjusting parameters to obtain a final tool exception detection model; and obtaining a current toolbox picture, cutting each current tool picture from the current toolbox picture according to the marking information, inputting the current tool pictures into the final tool abnormality detection model, and judging whether placement is abnormal or not. The tool abnormal placement detection method and system based on double-index metric learning can be widely applied to the field of image processing.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method and system for detecting abnormal tool placement based on dual-index metric learning. Background technique [0002] In aviation, the overhaul and maintenance of aircraft is an essential part of flight preparation. The tools used for aircraft maintenance are stored in a special tool box, and each tool has a specific storage position in the tool box. After the maintenance is completed, the maintenance personnel need to check whether there are abnormalities such as misplacement and leakage of the tools, so as to avoid problems such as taking the wrong tool next time or forgetting to take it back from the plane. At present, the abnormal placement detection of tools is mostly carried out manually. When there are many tools with similar shapes, this method is slow and error-prone; in addition, there are some automatic detection methods, which judge whether the tools are covere...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06N3/04G06N3/08
CPCG06T7/001G06T7/10G06N3/08G06T2207/10004G06T2207/20132G06T2207/30164G06N3/045Y02P90/30
Inventor 马锦华李璐圆严晓威陈曦
Owner SUN YAT SEN UNIV
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