Intelligent lamp pole LED screen defect detection method and device
By configuring a camera near the smart lamp pole, using YOLO and improved ShuffleNet model to automatically detect defects in street light LED screens, solving the problems of low efficiency and insufficient accuracy of traditional manual inspections, and achieving efficient and accurate defect detection and maintenance.
CN119648666APending Publication Date: 2025-03-18SHANGHAI UNIV OF ENG SCI +1
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Patent Information
- Application Number
- CN202411759997.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-03
- Publication Date
- 2025-03-18
AI Technical Summary
Technical Problem
Traditional street light LED screen detection relies on manual inspection, which is costly, low efficiency and easy to miss. The existing visual detection methods are insufficient in accuracy, complex equipment construction and high deployment cost.
Method used
The camera is configured near the smart lamp pole to collect LED screen image data, use the YOLO network to detect the target area, locate the LED screen area, and input it into the improved ShuffleNet model for defect detection classification, and generate defect categories and their location information.
Benefits of technology
Automatic, fast and efficient LED screen defect detection is realized, reducing manual inspection time and cost, improving maintenance efficiency and accuracy, and reducing deployment costs.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides an intelligent lamp pole LED screen defect detection method and device, and the method comprises the steps: collecting image data of an intelligent lamp pole LED screen through a camera disposed near an intelligent lamp pole, and carrying out the preprocessing of the collected image; performing target area detection on the preprocessed image by using a YOLO network, and identifying and positioning a specific area of the LED screen; an LED screen area positioned by the YOLO network model is input into an improved ShuffleNet model for defect detection and classification; and the detected defect category and position information thereof are used as fault output, so that subsequent maintenance personnel can deal with the fault in time. According to the invention, the defects of the LED screen can be automatically, quickly and efficiently detected, so that the time and cost of manual inspection are reduced, and the maintenance efficiency and accuracy are improved.
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