Detection Method for Installation Effect of Switch Machine Movable/Static Contact Groups Based on Key Point Recognition

Abstract

Objective Traditional methods for detecting the installation effect of SmMSC (switch machine movable/static contact) groups are slow, inaccurate, and susceptible to human errors. It is necessary to introduce a fast and high-precision image detection technology based on key point recognition, aiming to make research on the installation effect detection method for SmMSC groups. Method The detection process of the above-mentioned detection method is described in detail. 24 key recognition points of the SmMSC groups are introduced, along with the calculation method for the contact depth of moving contacts and spacing between the bases. Through test analysis of different combination models, the selected optimal key point recognition model is based on the pose detection algorithm in the YOLOv8 visual framework, incorporating the BiFormer dual-encoder attention mechanism and SCConv (spatial and channel reconstruction convolution) efficient convolution module. The functions of the auxiliary shooting frame and perspective correction transformation are also described. Result & Conclusion Recognition time of the optimal key point recognition model is only 1.3 milliseconds, with a recognition accuracy reached 96.3%. The installation effect inspection method of SmMSC groups based on key point recognition achieves an image recognition rate of 99.8% for the dynamic and static contact groups, with a calculation accuracy of ±0.1 mm, and an average recognition error lower than 0.3%, only 2 seconds for each group′s detection. It′s obvious that this method is significantly more intelligent and efficient compared to manual detection methods.

Keywords

• urban rail transit
• switch machine
• movable/static contact groups
• key point recognition