Loss detection method of water injection port cover based on deep learning
A technology of loss detection and deep learning, applied in image analysis, image enhancement, instruments, etc., can solve problems such as fault omission, low efficiency, and false detection
Active Publication Date: 2021-09-14
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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[0002] Since the current fault detection method of trains is still mainly based on manual viewing of images or on-site inspections, this method is inefficient, and it is easy to cause fault omissions, false detections, etc., and affect the safety of vehicles.
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[0037] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.
[0038] A method for detecting loss of a water injection port cover plate based on deep learning according to an embodiment of the present invention will be described below with reference to the accompanying drawings.
[0039] It should be noted that, in the case of no conflict, the various embodiments disclosed in the present application or the features included in the embodiments can be combined with each other.
[0040] figure 1 It is a flowchart of a method for detecting loss of a water injection port cover plate based on dee...
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The invention discloses a method for detecting the loss of a cover plate of a water injection port based on deep learning, which solves the problems that the existing manual detection is inefficient and easily causes omission of faults, false detection and the like. The invention comprises the following steps: establishing an image training set of the water injection port cover plate; obtaining a target convolutional neural network model, wherein the target convolutional neural network model includes an improved modified linear unit activation function; training the target convolutional neural network through the image training set The network model obtains the trained target convolutional neural network model, wherein the trained target convolutional neural network model includes the optimal parameter weight; utilizes the trained target convolutional neural network model to detect whether the water injection port cover plate of the image to be detected is Lost, if lost, an alarm signal will be sent. The method realizes automatic fault alarm instead of manual detection, improves detection efficiency and accuracy, is not affected by personnel's physiology and psychology, and greatly improves operation quality.
Description
technical field [0001] The invention relates to the technical field of train fault detection, in particular to a method for detecting loss of a water injection port cover plate based on deep learning. Background technique [0002] Since the current fault detection method of trains is still mainly based on manual image viewing or on-site inspection, this method is inefficient, and it is easy to cause fault omissions, false detections, etc., and affect the safety of vehicles. [0003] Therefore, automatic fault identification technology is of great significance in train detection, and a fault detection method that can realize automatic fault alarm and improve train operation efficiency is urgently needed. Contents of the invention [0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent. [0005] For this reason, the object of the present invention is to propose a method for detecting the loss of the water in...
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IPC IPC(8): G06T7/00
CPCG06T7/0008G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30248
Inventor 张庆宇
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD