Injector defect detection method based on semantic segmentation

Abstract

The invention provides an injector defect detection method based on semantic segmentation. The injector defect detection method comprises the following steps: S1: performing syringe image acquisition,S2, converting a json file into a png mask picture by using a code; S3, performing image processing, thus completing the manufacturing of an injector data set; S4, constructing a full convolutional neural network model; inputting the training data set into a semantic segmentation network; and iterating the model parameters to obtain a convergent segmentation model; S5, testing a model, inputtingthe test data set into a semantic segmentation network, and obtaining injector segmentation maps; S6, exporting a model file; and S6, carrying out defect detection through a semantic segmentation network. The injector defect detection method performs defect detection through the semantic segmentation network, automatically judges whether the injector has defects or not is faster and more accurate,is quicker and more accurate, compared with traditional manual detection, can improve the automation degree of the manufacturing process, can greatly reduce the enterprise cost, and meanwhile, can improve the productivity.

Description

technical field [0001] The invention relates to the field of its visual technology, in particular to a method for detecting syringe defects based on semantic segmentation. Background technique [0002] During the manufacturing process of syringes, there will inevitably be defective products, so the processing plant needs to sort out these defective products. In the past, syringe manufacturers used human eyes to judge their manufacturing defects. Due to the large number of syringes manufactured , relying entirely on people to detect manufacturing defects, not only the detection efficiency is low, but also the cost is high, the detection work requires a lot of manpower and material resources, and the final detection results will also affect the detection accuracy and efficiency due to human uncertainty. [0003] Therefore, it is necessary to provide a semantic segmentation-based syringe defect detection method to overcome this difficulty. Contents of the invention [0004] ...

AI Technical Summary

Problems solved by technology

[0004] The invention provides a syringe defect detection method based on semantic segmentation, which overcomes the traditional artificial syringe defect detection method, uses the semantic segmentation model for real-time syringe defect detection, and can be used for non-destructive and non-contact detection

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