Railway wagon loading video intelligent monitoring system

Abstract

The invention discloses a railway wagon loading video intelligent monitoring system. The system comprises a sensor unit, an image acquisition unit, a wagon number acquisition unit, a lamp control unit, a carriage segmentation unit, a transmission unit and an intelligent monitoring unit. The system comprises the following steps of positioning parts based on a Darknet deep learning framework and a Yolo neural network algorithm; carrying out abnormality detection on components by utilizing an abnormality detection algorithm, adopting different methods for different types of abnormality detection,adopting a customized high-definition color linear array camera, setting the sampling frequency of the camera in combination with the running speed of a train, obtaining a high-definition color imageof the train, and restoring real details. Meanwhile, the system is higher in environmental adaptability, and the imaging quality can be guaranteed under the conditions of rain, snow, night and the like which are unfavorable for operating personnel to come to the site by themselves; furthermore, the system is higher in intelligent degree, and abnormal detection of parts can be realized for multi-class state detection items.

Description

technical field [0001] The invention relates to the technical field of railway freight car state detection, in particular to an intelligent video monitoring system for railway freight car loading. Background technique [0002] In order to ensure that railway wagons are in a normal loading state, railway staff need to complete a large number of inspections in daily maintenance work. The traditional manual vehicle inspection method has low work efficiency and high work intensity, and requires high technical level of the inspectors, which is prone to problems such as omissions and misjudgments during long-term operations. Moreover, this method has certain requirements for the detection environment, and it is difficult to guarantee the detection quality in a bad weather environment, which is likely to cause a certain degree of safety hazard. At present, the cargo inspection system adopted by the station usually collects the loading information of trucks by video shooting, and t...

AI Technical Summary

Problems solved by technology

The traditional manual vehicle inspection method has low work efficiency and high work intensity, and requires high technical level of the inspectors, which is prone to problems such as omissions and misjudgments during long-term operations

Moreover, this method has certain requirements for the detection environment. It is difficult to guarantee the quality of detection in bad weather environments, and it is easy to cause a certain degree of safety hazards.

At present, the cargo inspection system adopted by the station usually collects the loading information of trucks by video shooting, and the inspectors conduct separate inspections through the images of truck loading. Since they do not have to visit the inspection site in person, the inspection efficiency is improved to a certain extent, but The items to be tested still need to be observed and judged one by one. With the wide application of information technology and the increasing number of vehicles, the demand for vehicle maintenance is gradually increasing. This method has been difficult to meet the needs of contemporary railway development. There is an urgent need for a new type of cargo inspection system with higher efficiency and higher precision

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