A steel bar end face recognition method based on a deep convolutional neural network

Abstract

The embodiment of the invention discloses a reinforcing steel bar end face recognition method based on a deep convolutional neural network. The method comprises the steps of cutting a reinforcing steel bar end face area image and a non-reinforcing steel bar end face area image through traversal of a sliding window; establishing an image library of the area images and dividing the images in the image library into training samples and test samples; applying the training sample to the training of the deep convolutional neural network, and determining learning parameters in the deep convolutionalneural network; After the convolutional neural network is trained, traversing is conducted on the original image of the end face of the reinforcing steel bar used for testing through a sliding window,and a result obtained after traversing at each time is transmitted to the trained convolutional neural network to be recognized; And marking red points on the identified end faces of the reinforcingsteel bars, then clustering the marked red points, finding out the centers of the identified end faces, and marking the centers on the original image for testing. According to the method, the strong feature learning capability of the deep convolutional neural network is fully utilized, so that the steel bar end face is efficiently and accurately identified.

Description

technical field [0001] The invention relates to the field of visual recognition, in particular to a method for recognizing steel bar end faces based on deep learning convolutional neural networks. Background technique [0002] In recent years, in Industry 4.0, factory automation plays a very important role, and replacing humans with machines is an inevitable trend in the future. While many factories produce by using automated equipment, they simply use PLCs or other simple automated systems to complete a single repetitive task. For industrial machine vision, traditional image processing algorithms are used to solve many problems, like defect detection, counting, measurement and location, etc. [0003] In the manufacturing process of the steel factory, it is a very important link to bundle and weld the steel bars and to label the steel bars. But at present, most factories still use manual welding labels when welding labels on the end faces of steel bars. Manual welding of ...

AI Technical Summary

Problems solved by technology

However, the traditional image processing method has the following disadvantages: the image conditions of the end face of the steel b

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