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Railway wagon floor damage fault identification method based on improved SLIC method

A railway freight car and fault identification technology, applied in character and pattern recognition, image data processing, instruments, etc., can solve the problems of low detection efficiency and high detection cost, achieve low detection cost, reduce false alarms, and be suitable for image segmentation. Effect

Active Publication Date: 2020-12-18
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problems of low detection efficiency and high detection cost in the existing fault identification method for the floor damage of railway wagons, and propose a method for identifying damage faults of the floor of railway wagons based on the improved SLIC method

Method used

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  • Railway wagon floor damage fault identification method based on improved SLIC method
  • Railway wagon floor damage fault identification method based on improved SLIC method
  • Railway wagon floor damage fault identification method based on improved SLIC method

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Embodiment Construction

[0063] The present invention builds high-definition imaging equipment around the rails of the trucks to photograph the high-speed running trucks to obtain high-definition line array images. According to the wheelbase information and the prior information of the position of the components, the floor image of the side is obtained. According to the characteristics of the image, the image is cropped and stitched, that is, the interference of the bogie area is removed, and the structural information of the image is preserved to the maximum extent, so that the ROI area can be extracted more accurately; the superpixel segmentation technology based on the region of interest is used to The image is segmented to obtain more accurate segmentation results; after the segmentation results are screened, SVM is classified, and whether it is a damaged area is judged according to the classification results. Upload the alarm to the faulty floor to ensure the safe operation of the train.

[0064...

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Abstract

The invention discloses a railway wagon floor damage fault identification method based on an improved SLIC method, and belongs to the technical field of railway wagon floor damage fault identification. According to the method, the problems of low detection efficiency and high detection cost of the existing railway freight car floor damage fault identification method are solved. According to the method, the original collected images are cut and spliced, the spliced images highlight the recognition area, and the aspect ratio is more suitable for image segmentation; secondly, optimizing the original SLIC algorithm, and modifying selection of seed points from grid-based selection to selection based on ROI (region of interest) and gradient information, so that the segmentation effect of the algorithm in the ROI is improved, and influence on judgment of an identification result due to poor segmentation effect is avoided; and finally, screening the segmented regions, and further classifying the damaged regions and easily confused images such as rainwater, mud stains and the like by adopting an SVM classifier, thereby reducing false alarm. The method can be applied to railway wagon floor damage fault identification.

Description

technical field [0001] The invention belongs to the technical field of fault identification of damaged floors of railway freight cars, and in particular relates to a method for identifying faults of damaged floors of railway freight cars based on an improved SLIC method. Background technique [0002] Floor damage of railway wagons is a common fault that endangers traffic safety. Traditional side bogie stations use manual image inspection for fault detection. Due to the fact that the inspectors are prone to fatigue and omissions during the work process, resulting in missed inspections and wrong inspections, which affect driving safety. Although the successful application of deep learning methods has improved the efficiency and accuracy of detection to a certain extent, there are some disadvantages in deep learning methods, such as the need for expensive GPU equipment investment and low detection efficiency. Therefore, it is necessary to study a method that can improve the de...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62G06T3/40
CPCG06T3/4038G06V20/59G06V10/267G06V2201/08G06F18/23G06F18/2411
Inventor 高恩颖
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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