Automatic Recognition and Detection System Based on Machine Vision

Abstract

In order to realize the automation of part identification and detection and the intellectualization of part production, this paper proposes a method of automatic identification and detection of parts based on machine vision (overall method). This paper combines machine vision with motion control theory and uses pulse coupled neural network (PCNN) edge detection and recognition algorithms to preliminarily design a set of machine vision automatic recognition and detection systems and carry out detection and recognition experiments on small parts such as relay covers. The experimental results show that the whole process of part size detection and recognition, including part feeding, image acquisition, size recognition, part screening, and reset of the experimental platform, can be completed within 5 s and can carry out high-precision dynamic recognition and detection when the part moves at a speed of 1 m/s. Through the correction and compensation of dynamic error, the detection accuracy of small rectangular parts with a length of 28.87 mm and a width of 12.36 mm can reach 0.04 mm. The visual inspection and recognition automation system improves the automation degree of parts inspection, improves the dimensional accuracy, optimizes the robustness of the system, and finally realizes the real-time screening and classification of parts and the efficient production of parts.