Method for judging state of railway wagon coupler knuckle pin based on image recognition and deep learning

Abstract

The invention discloses a method for judging a state of railway wagon coupler knuckle pin based on image recognition and deep learning, and relates to the field of railway wagon fault judgment. The objective of the invention is to solve the problem that faults cannot be found in time easily caused by inaccurate detection results when the faults of trucks are detected manually in the prior art. According to the invention, manual detection is replaced by an image automatic identification mode, the fault identification detection efficiency and accuracy are improved, and the labor cost is reduced.Meanwhile, deep learning is applied to part positioning and fault detection, and the robustness and accuracy of the algorithm can be effectively improved. The small target in the image can be effectively positioned and recognized in a mode of performing coarse positioning by adopting the target detection network and then performing recognition in the positioned screenshot, so that the detection accuracy and precision are improved.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to view more

AI Technical Summary

Benefits of technology

This technology helps detect or identify any tiny objects on an object's surface more efficiently by automatically analyzing images without human interference. It does this through advanced techniques like convolutional neural networks (CNN) that are trained with data from training examples collected beforehand during testing. By doing these things we aim to reduce labor cost while still maintain high-quality results. Additionally, it suggests applying Deep Learning algorithms such as regression analysis to help better determine if there were smaller defects detected within the dataset. Overall, our technical effect will be improved automated imagery processing systems performance and reduced labor requirements compared to current methods.

Problems solved by technology

This patented technical problem addressed in this patents relating to railroad car coupling devices involves improving their ability to detect failures caused due to crackings that may develop into more significant damage if they happen too late after being detected. Current methods involve visual observation from outside sources like engine oil pressure monitoring systems (EBS) or machine vision cameras, but these techniques have limitations because there could also be other causes affecting the performance of the device itself.

Images