Industrial automation defect detection method based on deep learning

Abstract

The invention discloses an industrial automation defect detection method based on deep learning, and relates to the technical field of product defect detection. Complex feature extraction work is avoided, the method has relatively high accuracy and relatively good generalization capability; under the conditions of small defect ratio and complex detection background, an applicable detection model can be quickly trained, a detection method with a better detection effect than that of a traditional detection method is obtained, the trained target detection model YOLO-V3 is used for processing a shot picture, and whether a detected object exists in the picture or not is judged; and the photographed pictures are recognized by utilizing a neural network topology Inception-V3 image recognition model obtained by migration learning of small sample data deployed on a server, and whether the target positions have defects or not is judged. The method is mainly applied to industrial production linedetection occasions.

Description

technical field [0001] The invention relates to the technical field of product defect detection, in particular to the field of defect detection based on a deep learning-based industrial automation defect detection method and system. Background technique [0002] Due to the influence of various external factors such as vibration, temperature, and pressure, product defects will inevitably appear in industrial automation production. Defect detection refers to the detection of defects such as spots, dents, scratches, color differences, and defects on the product surface, and is one of the most important tasks in industrial quality control. With the development of the economy and the improvement of industrial production efficiency, based on the traditional manual defect detection method, not only the detection speed is slow and the efficiency is low, but also error-prone during the detection process, it is difficult to meet the high-efficiency requirements of modern industrial au...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the deficiencies of the prior art, the present invention aims to propose an industrial automation defect detection method and system based on deep learning, which can avoid complex feature extraction work, and has high accuracy and good generalization ability , which shows advantages when the proportion of defects is small and the detection background is complex

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