A real-time train oil unloading worker fall identification method

By using high-definition explosion-proof cameras and image processing technology in the oil unloading scenario on trains, the location and characteristics of workers can be automatically identified, enabling real-time monitoring and early warning of employee falls. This solves the problem of security personnel having difficulty monitoring multiple cameras and improves safety.

CN117315777BActive Publication Date: 2026-03-31SICHUAN HONGHE COMM CO LTD
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Patent Information

Application Number
CN202311216681.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-09-20
Publication Date
2026-03-31
Estimated Expiration
2043-09-20

AI Technical Summary

Technical Problem

During the unloading of oil from the train, security personnel had difficulty monitoring multiple cameras in real time, which resulted in emergencies such as staff falling not being detected in time, leading to poor safety.

Method used

Multiple high-definition explosion-proof cameras were used to capture images of the train unloading oil scene. Human detection models and key point models were used to detect human position bounding boxes, extract features, and identify falls, automatically outputting early warning prompts to avoid security personnel having to check the cameras in real time.

Benefits of technology

It enables real-time identification and early warning of staff falls, improving safety, eliminating the need for real-time manual monitoring, and reducing safety hazards.

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Abstract

The present application relates to the technical field of image processing, in particular to a real-time train oil unloading staff falling recognition method, device, equipment and storage medium, wherein the real-time train oil unloading falling recognition method comprises: acquiring a current frame picture shot by a camera, detecting a person position frame based on a person detection model; performing abnormal recognition on the detected person position frame; performing feature extraction and classification on the target person of the person position frame based on a key point model to obtain key point data and staff uniform classification data; judging whether the target person corresponding to the staff uniform classification data is a staff; performing falling recognition on the person position frame corresponding to all staff based on the key point data; and outputting a staff falling recognition result. Through target detection and key point fusion, the present application realizes person detection and feature extraction on the working staff working scene picture, recognizes whether falling occurs, and outputs a warning prompt information, so that the security personnel do not need to check the camera at all times, and the safety is high.
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