Machine vision based Vernier caliper reading technology research

Abstract

Accurate and fast access to Vernier caliper readings is a critical issue in automated verification of Vernier calipers. To address this problem, this paper proposes a machine vision-based algorithm for reading the Vernier caliper’s displayed value. The suggested method first employs threshold segmentation and template matching to determine the region of interest and obtain the main ruler digit position by alternate projection. Then, we apply the improved LeNet5 network to identify the main ruler of the Vernier caliper, Moreover, we developed the first and last inscription method for reading the decimal part of the Vernier caliper and established our data set for model training. Extensive experiments on reading the displayed value have demonstrated our algorithm’s accuracy, which achieves a displayed value reading accuracy of 100%. Compared to other methods, the proposed technique affords better stability and accuracy.

Keywords

• vernier calipers
• convolutional neural networks
• error averaging
• alternate projection