Design and Implementation of Anti-clamp System for Learning Platform Based on Deep Learning

In this paper, a kind of deep learning - based learning table automatic closing anti - clamping technology is proposed. Based on image processing, this technology builds a machine vision model of deep convolutional neural network, collects samples in sections for video analysis, conducts batch image preprocessing on massive data, trains corresponding models respectively, and then conducts real-time monitoring on the closing process of the learning desk. If there is an obstacle, the information will be fed back to the system to stop the closing of the learning desk. If not, continue to perform the closing action until the learning table is completely closed. This method through the actual test, has high response speed and accuracy of the obstacle detection, detection blind area is smaller, the advantages of non-contact and active prevention clip. At the same time, it has strong adaptability, low requirements on hardware, and has a very good application prospect.