Abnormal street lamp detection system and method based on YOLOv8 model

Through the abnormal street light detection system based on the YOLOv8 model, the problems of complex street light abnormal detection process, poor real-time and high cost in the existing technology are solved, and efficient, real-time and low-cost street light abnormal detection effect is achieved.

CN119672629APending Publication Date: 2025-03-21SHANGHAI UNIV OF ENG SCI +1
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Patent Information

Application Number
CN202411722381.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

The existing street lamp abnormality detection methods have problems such as complex process, poor real-time and high cost, making it difficult to achieve efficient, real-time and low-cost detection.

Method used

The abnormal street light detection system based on the YOLOv8 model is adopted, and the image acquisition module, object detection module and abnormal street light recognition module are used to automatically detect and state analysis using computer vision and deep learning technology to achieve real-time monitoring and recognition.

Benefits of technology

It improves the reliability and accuracy of detection, reduces manual intervention and false alarms, reduces system deployment and maintenance costs, and achieves efficient and real-time street light abnormality detection.

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Abstract

The invention provides an abnormal street lamp detection system and method based on a YOLOv8 model, the system comprises an image acquisition module, a target detection module and an abnormal street lamp identification module, the image acquisition module is used for acquiring a monitoring image on a road in real time, the target detection module adopts the YOLOv8 model to carry out target detection on image data, and the abnormal street lamp identification module is used for identifying the abnormal street lamp. Identifying a street lamp area in the image, positioning a street lamp in the image and marking the position of the street lamp; and the abnormal street lamp identification module analyzes the detected street lamp state by using image features, performs multiple times of abnormal detection on multiple street lamp images collected in the same time period by adopting a multiple-time identification strategy, and judges a final result through a voting mechanism, thereby identifying the abnormal street lamp. According to the invention, automatic detection and state analysis are carried out on the street lamps by using the YOLOv8 model, so that real-time monitoring and identification of abnormal street lamps are realized, and the intelligent management level of an urban road illumination system is improved.
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