The invention provides an industrial glass defect detection method based on Visionmaster. The industrial glass defect detection method is provided by aiming at the pain point of current glass defect detection and combining the actual demand of an enterprise. A traditional industrial glass defect detection method has many problems and deficiencies, such as poor environmental adaptability, lack of robustness, low precision, dependence on manual adjustment and the like. In order to solve the problems, the innovation comprises the following steps: preprocessing an image, detecting whether the glass is transversely broken or longitudinally divided, detecting glass edge defects, detecting glass surface defects, and summarizing results to judge the qualification of the glass. Firstly,
noise interference is removed through an
image processing technology, the position and the edge of a glass area are obtained, and an
image enhancement means is used for improving the
contrast ratio, so that an image is clearer; based on Visionmaster, whether glass is transversely
cut or longitudinally divided, whether edge defects exist and whether surface defects exist are judged by utilizing a traditional
visual technology and setting a proper threshold range. And finally, determining whether the industrial glass is qualified or not by summarizing detection results of all links. And if not, determining the defects of the glass. The industrial glass defect detection method has the advantages that the coding difficulty is reduced, the parameter adjustment process in the detection process is simplified, and the accuracy, universality and anti-interference capability of industrial glass defect detection are improved; and meanwhile, the labor cost is reduced, the product quality is better guaranteed, and the detection speed and efficiency are improved. In addition, the method has high
interpretability and adaptability, and is suitable for complex and changeable industrial field environments. According to the method, while the production efficiency is improved, challenges existing in a traditional method are effectively handled, and a more effective and reliable detection method is provided for the industrial
glass production process.