Fault detection method, system and device for LED traffic guiding screen

Abstract

The invention discloses a fault detection method, system and device for an LED traffic guiding screen. The method comprises steps that a template image is generated according to a standard traffic guiding screen image; a to-be-detected image is generated according to a to-be-detected traffic guiding screen image; an image subtraction method is employed to carry out damaged area detection on the to-be-detected image according to the generated template image; blurred screen detection on the to-be-detected image is carried out according to the generated template image; the system comprises a first generation module, a second generation module, a damaged area detection module and a blurred screen detection module; the device comprises a memory and a processor. The method is advantaged in the image subtraction method is utilized to carry out damaged area detection on the to-be-detected image, computational complexity is substantially reduced, work efficiency is improved, moreover, the blurred screen detection function is newly added, a disadvantage of manpower and material resource consuming existing in a traditional manual inspection mode is solved, efficiency is higher, maintenance cost is reduced, the method can be widely applied to the digital image processing field.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a fault detection method, system and device of an LED traffic guidance screen. Background technique [0002] Glossary: [0003] Machine vision: refers to the use of computers to simulate human visual functions, but it is not just a simple extension of the human eye, but more importantly, it has a part of the function of the human brain, that is, extracting information from images of objective things, processing them and It is understood and ultimately used for practical detection, measurement and control. The machine vision system captures the image through the camera, and then transmits the image to the processing unit. Through digital processing, the size, shape, color, etc. are judged according to the pixel distribution, brightness, color and other information. Specifically, the processing flow of a complete machine vision system is as follows image 3 shown. [0004...

AI Technical Summary

Problems solved by technology

The existing LED dead point detection method obtains the standard image and the image to be detected, and compares all the pixels in the two images one by one, so as to obtain the corresponding number of dead points and the position of the dead point. This dead point detection method The amount of calculation is huge. For images with a large number of pixels to be detected, it takes a lot of time to perform comparison operations, which is inefficient and cannot display the detection results in real time; fault detection

Due to the wide distribution of LED display screens, manual inspections require a lot of manpower and material resources, and the detection efficiency is not high, and problems may not be found in time

If the LED display with blurred screen cannot be detected in time, not only will the color of the display screen be very rough or mosaic, but it may also cause the expansion of the blurred screen fault, which will increase the cost of follow-up maintenance.

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