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Steel ball surface defect detection method based on single stage

A defect detection, single-stage technology, applied in image data processing, instruments, calculations, etc., can solve problems such as poor accuracy and slow detection speed, and achieve the goal of reducing labor force, reducing production costs, and improving detection efficiency and accuracy Effect

Pending Publication Date: 2021-08-06
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0003] The purpose of the present invention is to avoid the problems of poor accuracy and slow detection speed in the steel ball surface defect detection method. This paper proposes a single-stage steel ball surface defect detection method

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  • Steel ball surface defect detection method based on single stage
  • Steel ball surface defect detection method based on single stage
  • Steel ball surface defect detection method based on single stage

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[0024] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described here are only used to explain the present invention and not to limit the present invention.

[0025] like figure 1 As shown, the image data is collected through the steel ball surface image acquisition system based on machine vision, and the collected images are preprocessed, that is, the image augmentation is performed on the data set first, and then the data set is marked with the tool LabelImg, and the marked The data set is divided into training set, verification set and test set, of which 80% is used as training data set, 10% is used as verification data set, and 10% is used as test data set, and the pre-training model is constructed and trained and adjusted. The trained YOLOv4 model is obtained, and finally t...

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Abstract

The invention discloses a steel ball surface defect detection method based on a single stage, and compared with traditional manual detection, the detection method has higher detection precision, detection efficiency and robustness. The single-stage detection method is based on a YOLOv4 network structure pre-training model. The method mainly comprises the following steps of steel ball surface image data acquisition, image augmentation, data set image annotation, data set division, pre-training model construction, model training and model verification. The model of the method can automatically extract the characteristics of the surface defects of the steel ball, and can accurately and quickly detect the positions of various defects on the surface of the steel ball. According to the method, a Python programming language is used, a Keras framework is used as a front-end function to realize, Tensorflow is used as a rear-end data processing, and GPU (NVIDIA, GTX1080Ti) is used for model training, model verification and model testing to obtain corresponding evaluation indexes and test results. According to experimental results, the method can be used for quickly and accurately detecting the surface defects of the steel balls.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a single-stage steel ball surface defect detection method. Background technique [0002] Due to the influence of technical factors such as different raw material batches of steel balls, the precision of processing machine tools, and process control during processing, local fine surface defects will occur during the processing of steel balls. Traditional manual detection has the problems of low detection accuracy, low detection efficiency and poor anti-interference. Therefore, the target detection method based on deep learning came into being and is widely used in industrial detection technology, such as Fast RCNN, Faster RCNN, SSD and other target detection models, but this detection method is not suitable for unbalanced defect types and small targets When there are defects, the accuracy of the model is not good, and the detection speed is slow. Contents of th...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0004G06T2207/20081G06T2207/20084G06T2207/30136
Inventor 王义文王恺娇刘立佳周丽杰
Owner HARBIN UNIV OF SCI & TECH
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