Product surface defect detection method, system and device and medium

Abstract

The invention discloses a product surface defect detection method, system and device and a medium. The method comprises the following steps: constructing a picture reconstruction network; collecting defect-free picture method training pictures; training a picture reconstruction network by using the training picture to obtain a trained picture reconstruction network; collecting a to-be-processed product surface defect picture; inputting the to-be-processed product surface defect picture into the trained picture reconstruction network, calculating feature values of the to-be-processed product surface defect picture and the reconstructed picture, calculating a feature value difference value of the to-be-processed product surface defect picture and the reconstructed picture, and obtaining a product surface defect detection result based on the feature value difference value. According to the method, surface defect detection is carried out only by using good pictures, and the surface defectrepairing capability is learned by reconstructing the good pictures on the basis of the full convolutional neural network, so that the pixel-level recognition capability for various surface defects isachieved.

Description

technical field [0001] The present invention relates to the technical fields of intelligent manufacturing and artificial intelligence, in particular to a product surface defect detection method, system, device and medium. Background technique [0002] The surface defect detection algorithm based on machine vision mainly includes traditional vision methods and deep learning methods. Traditional visual methods are mainly based on artificially designed visual features, such as watershed algorithm, LBP feature method, etc. Deep learning methods are mainly based on convolutional neural networks, such as Faster RCNN algorithm, MASK RCNN algorithm, etc. The use of deep learning methods in automatic surface defect detection algorithms is becoming increasingly popular. [0003] Currently, in the surface defect detection methods using deep learning, supervised learning methods such as target detection algorithms or semantic segmentation algorithms are mainly used. This type of metho...

AI Technical Summary

Problems solved by technology

This type of method mainly faces four problems in practical application. First, it needs to collect a large number of defect pictures as training data. The collection of low-probability defect pictures in actual production requires high time and labor costs; second, based on the existing The automatic surface defect detection algorithm for training pictures cannot cope with the unknown defect manifestations that may occur in production. The method based on supervised learning can achieve a better generalization effect on the defect forms contained in the training data set, while the actual production The sudden unknown new defects in the process do not exist in the existing training pictures, and the algorithm may miss detection of defects, resulting in production losses; third, a large number of manually labeled defect pictures, object detection and semantic segmentation, etc. The cost is high and the time required is long, which leads to the slow response of the algorithm to changes in production conditions and poor timeliness; fourth, it is difficult to adapt to new product pictures. In supervised learning, good product pictures without defects are used as negative For learning from pictures, the background of new product pictures that do not exist in the training data may change greatly, which may cause the algorithm to over-check the defects. To solve this problem, it is necessary to invest in manpower to collect and label new product pictures in time , and then optimize the model, resulting in slower algorithm update

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