Automatic identification and detection system of digital vernier caliper jumps based on CNN

Abstract

In order to solve the key problem of hop count detection of digital vernier calipers before leaving the factory, and the traditional manual detection method requires high labor and is inefficient, a digital caliper based on convolutional neural network and OpenCV computer vision library is designed. Identify automatic detection systems. Firstly, according to the spatial position and size distribution characteristics of the digital display screen of the digital vernier caliper, the digital display area image of the caliper is collected by calling a drive-free USB industrial camera in opencv-python, and preprocessed, and then the convolutional neural network is used. The (CNN) model conducts data training on the image samples of the digital display area, and finally the test part conducts a digital recognition detection experiment of 20 frames per second on the digital display area. The experimental results show that the digital vernier caliper automatic identification and detection system based on the convolutional neural network model has a digital recognition accuracy rate of 99.32%, and the accuracy fluctuation is only 0.68%, which better realizes the need to log the digital vernier caliper before leaving the factory. Hop detection is performed for this purpose.