Synthesis method of a neural network training sample in part surface defect detection

Abstract

The invention belongs to the technical field of surface defect detection, and particularly provides a synthesis method of a neural network training sample in part surface defect detection. The training sample synthesis method comprises the following steps: S1, acquiring an image of a defective part sample; S2, acquiring a defective image from the image of the defective part sample; And S3, extracting image features of the defective image, and adding the disturbance into the image features to generate a training sample. Through the method, the training sample is obtained; only a small number ofdefective parts need to be obtained; A small number of images of the surface of a defective part are obtained; flaws existing in the image are extracted to obtain images of various flaws; According to the method, the image features of the flaws are extracted from the images with various flaws, and then corresponding disturbance is added to the image features to generate a large number of trainingsamples, so that the training requirements of the neural network are met, and the problems that the training samples for neural network training are difficult to obtain and a large number of trainingsamples cannot be obtained are solved.

Description

technical field [0001] The invention belongs to the technical field of surface flaw detection, and specifically provides a method for synthesizing training samples of a neural network in part surface flaw detection. Background technique [0002] The traditional detection method for surface flaws of precision parts is mainly manual detection. The manual detection method is limited by factors such as the working status of the inspectors, the level of detection skills, and the level of proficiency, so it is inevitable that false detections and missed detections will occur. Using manual detection of surface defects of parts has high labor intensity, low detection efficiency and high error rate. [0003] In recent years, with the remarkable effect of deep learning in semantic recognition and image understanding, the object detection method using neural network to automatically train feature expression has been developed more and more rapidly (Krizhevsky A, Sutskever I, Hinton G ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the difficulty in obtaining training samples for neural network training and the problem that a large number of training samples cannot be obtained, the present invention provides a synthesis of training samples for neural networks in part surface flaw detection. method, the synthesis method includes: step S1: acquiring an image of a part sample with defects; step S2: acquiring a defect image from the image of a part sample with defects; step S3: extracting the image features of the defect image and adding disturbance to the image features to generate training samples

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