Automatic and Accurate Measurement of High Precision Thread Based on AA R2Unet and HMM

Abstract

The thread measurement methods based on machine vision are easily disturbed by the environment(e.g. dust,iron filings, oil stains, etc.), resulting in inaccurate measurement results. This paper improves the R2 Unet model by adding the Attention mechanism, and proposes an external thread measurement method based on AA R2 Unet and hidden Markov model(HMM). Firstly, to overcome the interference of dust, iron filings et al, the AA R2 Unet model was designed to identify and extract the external threads. Secondly, the feature information on gradient direction of thread edge points is calculated, HMM was used to classify the thread edge points so that the threaded parts can be placed at any angle during the measurement. Finally, the method with the gathered dataset was evaluated. The results show that the segmentation accuracy of the thread edge extraction method based on AA R2 Unet is 95.92%, the classification accuracy of thread edge points based on HMM is above 86% and the comprehensive measurement error is within 0.01 mm.

Keywords

• unet mode
• attention mechanism
• hidden markov model
• thread measurements