Steel pipe surface defect detection method based on machine vision

In the process of steel pipe production,due to the influence of various factors such as raw materials and processing technology,various defects may appear on the surface of steel pipes,such as cracks,scratches,folds,pits,warping,roller marks,etc.These defects not only affect the appearance quality of steel pipes,but may also cause safety accidents such as leakage and breakage of steel pipes during use.Therefore,it is very important to perform high-precision detection of surface defects of steel pipes.Most traditional detection methods rely on manual labor,but this method has problems such as low efficiency,susceptibility to environmental influences,high labor intensity,and easy to produce missed detection and false detection.With the development of computer technology and artificial intelligence,machine vision technology has gradually been applied to steel pipe surface defect detection.Its high precision and high efficiency provide a new solution for steel pipe quality inspection.

1.Characteristics and challenges of steel pipe surface defect detection

The surface defect detection of steel pipes has its uniqueness,which is mainly reflected in the following aspects:

Various inspection objects:There are various types of steel pipes,including hot-rolled seamless steel pipes,hot-rolled strip steel,etc.The surface roughness,reflectivity and other characteristics of these products are different,which puts forward different requirements for inspection equipment and algorithms.

Complex testing environment:Steel pipes may be affected by vibration,assembly errors and other factors during the production process,resulting in errors in image acquisition during testing,which increases the difficulty of image processing.

Various defect types:There are various types of defects on the surface of steel pipes,including scratches,scrapes,holes,scars,pits,etc.The distribution and size of these defects are irregular and the shapes are complex,which increases the complexity of detection.

High real-time requirements:During the steel pipe production process,it is necessary to detect defects on the steel pipe surface in real time so that they can be discovered and handled in time to ensure product quality and production efficiency.

In practical applications,steel pipe surface defect detection based on machine vision faces the following challenges:

Uneven illumination:The curved outer surface of the steel pipe is prone to uneven illumination,which causes the features of the defective area to be covered,increasing the difficulty of image processing.

False defect interference:The surface of hot-rolled seamless steel pipe is covered with a large amount of iron oxide scale,which may cause various false defects and affect the accuracy of detection.

Vibration interference:During the inspection process,the steel pipe may vibrate due to the influence of curvature,out-of-roundness and surface protrusion defects,resulting in errors in image acquisition.

Difficulty in dynamic detection:When implementing dynamic detection,the overlap between the light source illumination area and the camera field of view area will decrease,resulting in uneven light distribution and affecting the detection effect.

2.Key technologies of machine vision inspection system

The steel pipe surface defect detection system based on machine vision mainly includes lighting system,image acquisition system,image processing system and defect recognition system.

Lighting system

The lighting system is a key part of the machine vision inspection system,which directly affects the quality of image acquisition and the difficulty of subsequent image processing.In the detection of surface defects of steel pipes,bright field lighting is usually used,which is beneficial to form a high contrast between the surface defects of steel pipes and the background.The selection of light source of the lighting system,the position relationship between the camera and the light source,etc.need to be carefully designed according to the characteristics of the steel pipe and the inspection requirements.

(1)Light source selection:According to the principle of steel pipe surface image acquisition,a linear array light source needs to be selected to achieve illumination to ensure that the light intensity is concentrated and uniform within the field of view.LED light sources are widely used in machine vision inspection systems due to their high efficiency,low power consumption,long life,high safety and good controllability.

(2)Position relationship:In the optical path design,it is necessary to determine the position of the linear array camera,linear light source,etc.to ensure that the field of view can cover the surface of steel pipes of different lengths.At the same time,it is necessary to ensure that the center line of the linear array light source and the length direction of the linear array camera field of view are as close to the same line as possible to reduce uneven illumination.

Image acquisition system

The image acquisition system is mainly composed of a camera and a lens,which is responsible for converting the image of the steel pipe surface into a digital signal for subsequent image processing system processing.

(1)Camera selection:Line array cameras are mainly divided into two types:CCD and CMOS.CMOS cameras have the advantages of flexible image capture,high sensitivity,wide dynamic range,high resolution,low power consumption and excellent system integration.At the same time,they are more affordable than CCD sensors,so they are widely used in machine vision inspection systems.

(2)Lens selection:Lens selection includes the determination of parameters such as focal length and imaging target size.Focal length is an important parameter of the lens and needs to be determined by factors such as object distance.According to the imaging principle,the focal length can be calculated by a formula.

Image processing system

The image processing system is mainly responsible for preprocessing,feature extraction and defect identification of the collected digital images.

(1)Preprocessing:Preprocessing includes operations such as image denoising,enhancement,and binarization to improve the quality of the image and provide a basis for subsequent feature extraction and defect recognition.

(2)Feature extraction:Feature extraction is a key step in the image processing system.It is necessary to extract features that can describe defects from the image.For defects on the surface of steel pipes,it is necessary to select features with good discrimination to form feature vectors,such as saturation,pixel distribution,target image edge,brightness and other information in the image.

(3)Defect recognition:Defect recognition is the ultimate goal of the image processing system.It is necessary to use advanced algorithms to perform feature recognition on the image based on the extracted feature vectors,evaluate the results of feature recognition,and output the final defect results,including defect type,size,angle,number,qualified or unqualified,etc.

Defect Identification System

The defect recognition system is mainly based on machine learning or deep learning algorithms to train and classify the extracted feature vectors to achieve automatic recognition of surface defects of steel pipes.

(1)Algorithm selection:Commonly used machine learning algorithms include support vector machine(SVM),random forest(RF),neural network(NN),etc.Deep learning algorithms include convolutional neural network(CNN),recurrent neural network(RNN),etc.According to the characteristics of steel pipe surface defects and detection requirements,select appropriate algorithms for training and classification.

(2)Model training:During the model training phase,a large number of steel pipe surface defect images need to be collected as training samples to train and optimize the algorithm.By continuously adjusting the algorithm parameters and model structure,the recognition accuracy and generalization ability of the model can be improved.

(3)Online detection:In the online detection stage,the collected steel pipe surface images are input into the trained model for real-time detection and recognition.Based on the output results of the model,it is determined whether there are defects on the steel pipe surface and corresponding treatment suggestions are given.

3.Application Examples and Effects of Machine Vision Inspection System

The steel pipe surface defect detection system based on machine vision has been widely used in many fields and achieved remarkable results.

Oil and gas pipeline engineering:In oil and gas pipeline engineering,non-destructive testing of steel pipes is crucial.The steel pipe surface defect detection system based on machine vision can achieve high-precision detection of the steel pipe surface,detect and deal with defects in a timely manner,and ensure the safe operation of the pipeline.

Iron and steel smelting industry:In the iron and steel smelting industry,the production quality of steel pipes directly affects the quality of products and production efficiency.The steel pipe surface defect detection system based on machine vision can realize comprehensive detection of the steel pipe surface and improve product quality and production efficiency.

Machinery manufacturing industry:In the machinery manufacturing industry,steel pipes are one of the important components,and their surface quality directly affects the performance and service life of the machinery.The steel pipe surface defect detection system based on machine vision can achieve high-precision detection of the steel pipe surface and ensure the performance and service life of the machinery.

Through practical application,the steel pipe surface defect detection system based on machine vision has achieved remarkable results.On the one hand,the system can achieve high-precision detection of the surface of steel pipes,timely discover and deal with defects,and improve product quality and production efficiency;on the other hand,the system can reduce the labor intensity of manual inspection,improve the real-time and accuracy of inspection,and provide strong support for the company's intelligent upgrade.

IV.Future Development Trends and Prospects

With the continuous development of computer vision technology and three-dimensional measurement technology,the steel pipe surface defect detection system based on machine vision will develop towards higher precision,more intelligence and wider application scenarios.

Higher-precision detection technology:With the continuous optimization of sensors and image processing algorithms,the accuracy of machine vision detection technology will be further improved,enabling more detailed detection of surface defects of steel pipes.

Smarter inspection systems:By introducing artificial intelligence and machine learning technologies,future machine vision inspection systems will be more intelligent and able to automatically adapt to different production environments and defect types,improving inspection accuracy and efficiency.

Wider application scenarios:In addition to steel pipe surface defect detection,machine vision inspection technology can also be applied to surface quality inspection of other metal materials,high-precision parts assembly and other fields,providing strong support for the intelligent upgrading of the manufacturing industry.

In short,the steel pipe surface defect detection system based on machine vision has significant advantages in improving product quality,reducing labor intensity,and improving detection real-time and accuracy.With the continuous advancement of technology and the expansion of application scenarios,the system will play a more important role in steel pipe production and quality inspection.