Human body shoulder flashing light detection alarm method and device based on deep learning

The human shoulder flash detection method optimized by deep learning solves the shortcomings of traditional methods in terms of environmental adaptability and real-time performance. It achieves high-precision, stable and consistent shoulder flash status detection and real-time alarm, and is suitable for safety management in complex environments.

CN121661681APending Publication Date: 2026-03-13SHANGHAI WONDERTEK SOFTWARE CORP LTD
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Patent Information

Application Number
CN202511866299.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-11
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

Traditional visual methods suffer from poor environmental adaptability, high light sensitivity, and high false detection rate in human shoulder flash recognition, while motion detection-based methods have poor real-time performance and unstable accuracy.

Method used

We employ a deep learning-based human shoulder flash detection method. By optimizing the detection network (enhanced attention mechanism and multi-scale fusion) and combining it with improved tracking algorithms (such as feature extraction optimization and multi-target association), we can improve recognition accuracy and tracking stability. Furthermore, we enhance real-time processing capabilities and resource utilization through system innovation.

Benefits of technology

It achieves high-precision, stable and consistent shoulder flash light status detection in complex environments, can provide real-time alarms for abnormal shutdown status, supports end-to-end automated safety management, and is suitable for 24-hour real-time monitoring systems.

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Abstract

According to the human body shoulder flashing light detection and alarm method and device based on deep learning, a video stream containing a portrait is acquired in real time, and a video frame is preprocessed; based on the preprocessed video frame, human body detection and human body key point detection are sequentially carried out through a target detection model, and a shoulder region of interest corresponding to a human body is extracted based on key point coordinates; respectively extracting a time sequence feature, a brightness change feature and a frequency feature of the extracted shoulder region-of-interest image sequence; and fusing the time sequence characteristics, the brightness change characteristics and the frequency characteristics, classifying the states of the shoulder flashing light, judging the turn-on or turn-off state of the shoulder flashing light, and outputting a state classification result. The target recognition performance is improved by optimizing the detection network, so that the accuracy is improved, and the tracking stability is improved by improving the tracking algorithm such as feature extraction optimization and multi-target association enhancement, so that the state judgment is more accurate.
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