CN111047556A - Strip steel surface defect detection method and device - Google Patents
Strip steel surface defect detection method and device Download PDFInfo
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Abstract
The embodiment of the invention provides a strip steel surface defect detection method, which comprises the following steps: acquiring a surface image of the strip steel; performing defect detection on the image by adopting a rapid two-classification detection method, judging whether the surface image of the strip steel contains defects, if so, performing accurate detection and classification on the surface image of the strip steel containing the defects by adopting a pyramid structure defect detection method, and determining the positions and the types of the defects contained in the surface image of the strip steel; if not, performing defect detection on the strip steel surface image by adopting an image processing method, determining whether the strip steel surface image containing the defects contains the defects, if so, performing accurate detection and classification on the strip steel surface image containing the defects by adopting a defect detection method of a pyramid structure, determining the positions and the types of the defects contained in the strip steel surface image, and if not, outputting the defects; according to the technical scheme of the invention, the real-time and accurate detection effect can be achieved, and the requirements of real-time performance and defect detection accuracy are met.
Description
Technical Field
The invention relates to the technical field of target detection, in particular to a method and a device for detecting surface defects of strip steel.
Background
With the rapid development of economy in China, the steel industry is used as a pillar industry of the manufacturing industry, and is rapidly developed in recent years, and strip steel is used as one of important products of the steel industry and is applied to various fields of industrial production, manufacturing, aerospace and the like. The surface quality of the strip steel seriously determines the final product performance, and the surface defect is an important factor influencing the surface quality of the strip steel, so the detection of the surface defect of the strip steel and the improvement of the surface quality of the strip steel have important practical significance.
Conventionally, the detection of the surface defects of the strip steel mainly adopts a manual detection mode, and whether the surface has defects is observed through human eyes. Due to the continuous improvement of the production speed, the defect information cannot be reliably captured by a manual detection method, and the evaluation standards of detection results are different under the influence of light, environment and physical factors of detection personnel. The manual detection method is gradually difficult to adapt to the development requirement of the modern steel industry, most factories can erect an area-array camera on a production line at present to detect the defects of the shot strip steel picture, the current commonly used defect detection method is to process the image of the shot picture, and the detection and the positioning of the surface defects of the strip steel are achieved according to the optimization and the improvement of the image processing method, but the method has lower defect detection accuracy and can not accurately position and identify the defect types.
In recent years, with rapid development of internet, big data, scientific technology and the like, an online detection system based on machine vision has been successfully applied to a production line of strip steel, and can realize efficient detection of surface defects of the strip steel. This type of inspection apparatus relies on a high speed camera to take images of the material on the production line and a computer to analyze whether and what defects are present. The existing equipment usually uses the traditional image processing algorithm to detect defects, such as Fourier transform and the like, but because the defects have various forms and low actual defect detection accuracy rate, manual secondary recheck is needed. With the rapid development of the deep learning detection algorithm, the detection accuracy is greatly improved, but due to the limitation of equipment, the integration of the deep learning detection algorithm on a production line is difficult to detect the defects of the strip steel on the production line running at high speed in real time.
In summary, no detection method for detecting the surface defects of the strip steel can realize real-time and accurate detection at present.
Disclosure of Invention
The invention aims to provide a method for detecting the surface defects of strip steel, so as to achieve real-time and accurate detection effect and meet the requirements of real-time performance and defect detection accuracy.
In order to achieve the above object, in one aspect, an embodiment of the present invention provides a method for detecting surface defects of strip steel, where the method includes:
acquiring a surface image of the strip steel;
performing defect detection on the surface image of the strip steel by adopting a rapid two-classification detection method, judging whether the surface image of the strip steel contains defects, if so, performing accurate detection and classification on the surface image of the strip steel containing the defects by adopting a defect detection method of a pyramid structure, and determining the positions and the types of the defects contained in the surface image of the strip steel;
if not, performing defect detection on the surface image of the strip steel by adopting an image processing method, determining whether the surface image of the strip steel contains defects, if so, performing accurate detection and classification on the surface image of the strip steel containing the defects by adopting a defect detection method of a pyramid structure, determining the positions and the types of the defects contained in the surface image of the strip steel, and if not, outputting the defect-free image.
On the other hand, the embodiment of the invention provides a device for detecting the surface defects of strip steel, which comprises:
the image acquisition unit is used for acquiring a surface image of the strip steel;
the first detection unit is used for detecting the defects of the surface image of the strip steel by adopting a rapid two-classification detection method and judging whether the surface image of the strip steel contains the defects or not;
the second detection unit is used for detecting the defects of the strip steel surface images of which the defects are not detected by the first detection unit by adopting an image processing method, and determining whether the strip steel surface images contain the defects;
and the third detection unit is used for accurately detecting and classifying the band steel surface images containing the defects determined by the first detection unit and the second detection unit by adopting a defect detection method of a pyramid structure, and determining the positions and the types of the defects contained in the band steel surface images.
The technical scheme has the following beneficial effects: the two-stage detection method for the surface defects of the strip steel can quickly and accurately detect the positions and the types of the defect information on the surface of the strip steel. Through the first-stage binary detection, most of images without defects can be rapidly filtered; the position and the category of the defect information on the surface of the strip steel can be accurately detected by the second-stage defect detection method of the pyramid structure.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for detecting surface defects of strip steel according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a device for detecting surface defects of strip steel according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flow chart of a method for detecting surface defects of strip steel according to an embodiment of the present invention, and in order to make the objects, technical solutions, and advantages of the present invention more clearly understood, the present invention will be described in further detail with reference to the accompanying drawings and the embodiment.
The invention is based on the following empirical rule found in actual production, taking the detection of a whole strip steel with the length of about 100 meters as an example, the strip steel is divided into a plurality of non-overlapping rectangular images with the width and the height of 10 centimeters at each position, wherein more than half of the rectangular images are smooth surfaces without any defects and suspicious defects, about half of the rest rectangular images are non-defective surfaces with non-smooth textures, and the rest rectangular images with defects. Based on the discovery, the invention does not directly detect the defects of all the images, but classifies the images and then detects the defects, and because most of the images do not contain the defects, the number of the images for detecting the defects can be reduced, the operation burden of a system is lightened, and the defect detection on the surface of the strip steel is rapidly completed.
A method for detecting surface defects of strip steel comprises the following steps:
s101, acquiring a shot surface image of the strip steel;
preferably, for the strip steel to be detected, the image of the surface of the strip steel is captured by the image capturing device, and the surface information of the strip steel is recorded by the image, for example, the surface image of the strip steel can be obtained by using an industrial camera.
S102, performing defect detection on the surface image of the strip steel by adopting a rapid two-classification detection method, judging whether the surface image of the strip steel contains defects, if so, accurately detecting and classifying the surface image of the strip steel containing the defects by adopting a defect detection method of a pyramid structure, and determining the positions and the types of the defects contained in the surface image of the strip steel;
preferably, the fast binary detection method comprises: the method comprises the steps of carrying out edge detection on a surface image of the strip steel, carrying out binarization on the edge image of the surface image of the strip steel according to a preset edge pixel value threshold, judging whether the surface image of the strip steel contains sharp defects or not according to the edge image after binarization, if so, carrying out accurate detection and classification on the surface image of the strip steel by adopting a defect detection method of a pyramid structure, and if not, carrying out defect detection on the surface image of the strip steel by adopting an image processing method.
In this embodiment, a pre-established image set is specifically described. Take a set X containing a certain number of defective images and a set Y containing two subsets [ Y1, Y2], defined as: y1 is the subset of normal images containing only smooth texture and Y2 is the subset of normal images containing non-smooth texture.
Further, the edge detection of the strip steel surface image comprises: extracting an edge image by using an edge operator, and binarizing the edge image according to a preset edge pixel value threshold, wherein the edge image binarization processing is to set the pixel value of a pixel point of the edge image of the strip steel surface image, the pixel value of which is lower than the preset edge pixel value threshold, to be 0, and set the pixel value of a pixel point, the pixel value of which is higher than the preset edge pixel value threshold, to be 255, extracting the edge image by using a Canny operator, and setting parameters of the Canny operator and a binarization pixel value threshold according to a known image set X, Y, and the method comprises the following steps: and under the premise that the number of the images in the set Y is judged to be less than a certain proportion by mistake, Canny operator parameters and a threshold value of a binary pixel value are adjusted, so that the images in the set X are judged to be the largest in number of the images containing the defects, the defect images can be detected as many as possible under the condition of being as fast as possible, the detection speed is favorably improved, whether the surface image of the strip steel to be detected contains sharp defects or not is judged according to the binary edge image, if yes, the obtained surface image of the strip steel is accurately detected and classified by adopting a depth learning algorithm, and the defect-containing images which cannot be identified after the step are defined are subjected to defect detection by adopting an image processing method.
Further, the edge detection of the strip steel surface image further comprises the following operators: for example, Roberts operator, which uses the difference between two diagonally adjacent pixels, is an operator that uses a local difference operator to find an edge, and only uses 2 × 2 neighborhood of the current pixel, and has a major disadvantage of high sensitivity to noise because few pixels are used to approximate the gradient; the Sobel operator is used for carrying out weighted average firstly, then differentiating and then solving gradient, carrying out convolution operation by using the Sobel operator, and taking the maximum value of two convolution kernels as an output bit of the point; the Prewitt operator is the same as the Sobel operator, each point in the image is convolved by the two kernels, and the maximum value is taken as output; and a Kirsch operator, each point in the image of the Kirsch operator being convolved with 8 masks, each mask responding most to a particular edge direction, the maximum of all 8 directions being output as an edge magnitude image.
S103, if not, performing defect detection on the surface image of the strip steel by adopting an image processing method, determining whether the surface image of the strip steel contains defects, if so, performing accurate detection and classification on the surface image of the strip steel containing the defects by adopting a pyramid structure defect detection method, determining the positions and the types of the defects contained in the surface image of the strip steel, and if not, outputting the defects.
Preferably, the image on the surface of the strip steel is subjected to image denoising and enhancement processing, so that defect information can be reserved, background noise can be removed, the denoised image defect information can be enhanced, the high-frequency characteristics of a defect region can be effectively amplified, the image defect information can be conveniently extracted, specifically, the image denoising is carried out by a non-local mean (non-local mean) method, and the high-frequency characteristics in a smooth texture are removed. The parameter determination method of the non-local mean method is as follows: according to the method, the high-frequency part in the normal surface texture of the strip steel can be effectively removed, the frequency distribution of the defect region is obviously different from the normal texture, the characteristics of the defect region cannot be simultaneously removed, and meanwhile, the high-frequency characteristics of the defect region and the unsmooth normal texture region are enhanced by using a self-adaptive histogram equalization method.
Preferably, the wavelet transform processing is performed on the denoised and enhanced image, the wavelet transform can simultaneously process the low-frequency long-term characteristic and the high-frequency short-term characteristic of the signal, the wavelet transform has good localization property, can effectively overcome the limitation of Fourier transform in processing non-stationary complex signals, has strong adaptability, is convenient for extracting the frequency domain information of the image defect after the wavelet transform processing, the frequency domain information is used for extracting the high-frequency information of the image of the defect region, and simultaneously performs high-pass filtering processing on the image after the wavelet transform processing, performs wavelet transform processing on all the images with undetected defects, and accurately detects and classifies all the processed images by adopting a defect detection method of a pyramid structure, thereby improving the detection accuracy and avoiding the occurrence of a missing detection event.
Further, the filtered image is binarized according to a preset image pixel value threshold, and for the filtered image, 255 is set if the pixel value is larger than the preset image pixel value threshold, and 0 is set if the pixel value is smaller than the preset image pixel value threshold.
Further, counting the number of the pixel values in the binarized image to be 255, wherein the number of the pixel values in the binarized image to be 255 is equal to the area of the defect region, if the area of the defect region is larger than a preset area value threshold, determining that the image contains the defect, and if the area of the defect region is smaller than a preset area value threshold, determining that the image does not contain the defect.
The parameter selection method of the three steps comprises the following steps: setting a proper wavelet transform, a filter, a binarization threshold value and a defect area threshold value parameter selection range, and searching by a parameter grid searching method to reduce the number of the normal pictures judged as defect pictures as far as possible on the premise that the number of the missed-judgment defect pictures is 0.
Preferably, the defect detection method of the pyramid structure includes:
the band steel surface image gradually generates a feature network with smaller and smaller size after passing through a depth convolution network, a feature map with shallower depth, namely larger size, can detect a target region with smaller area, a feature map with deeper depth, namely smaller size, can detect a target region with larger area, and the detection process is defined as pyramid structure defect detection.
Extracting the area of all defects in a certain number of pre-established defect image sets X to form an area set M, squaring each element in the M to obtain a rectangular side length set L, clustering the set L by using a K-means clustering (K-means) method, traversing the number of classes from 2 upwards to enable the K-means clustering loss to meet the requirement, namely stopping, wherein the number of the classes is the number of pyramid layers, and the clustering center is the size of a pyramid prior detection frame of each layer;
and generating a characteristic diagram of the strip steel surface image in each layer of pyramid, proposing candidate areas in each layer of pyramid characteristic diagram, and after removing the overlapped candidate areas by using a non-maximum inhibition method, proposing n positions which can be defect areas in each layer of pyramid.
And putting the n-pyramid-layer defect candidate regions into a convolution network, and outputting the defect possibility, defect types and fine-tuned region positions of each candidate region.
On the other hand, the embodiment of the invention provides a device for detecting the surface defects of strip steel, which comprises:
the image acquisition unit 21 is used for acquiring a strip steel surface image;
the first detection unit 22 is configured to perform defect detection on the strip steel surface image by using a fast two-classification detection method, and determine whether the strip steel surface image contains a defect;
the second detection unit 23 is configured to perform defect detection on the strip steel surface image with no defect detected by the first detection unit by using an image processing method, and determine whether the strip steel surface image contains a defect;
and the third detection unit 24 is configured to perform accurate detection and classification on the strip steel surface images containing the defects determined by the first detection unit and the second detection unit by using a defect detection method of a pyramid structure, and determine positions and types of the defects contained in the strip steel surface images.
Preferably, the first detection unit 22 includes:
and carrying out edge detection on the surface image of the strip steel, carrying out binarization on the edge image of the surface image of the strip steel according to a preset edge pixel value threshold, and judging whether the surface image of the strip steel contains sharp defects or not according to the edge image after binarization.
Preferably, the second detecting unit 23 includes:
carrying out image noise reduction processing on the surface image of the strip steel;
carrying out image enhancement processing on the noise-reduced strip steel surface image;
performing wavelet transform processing on the enhanced image, extracting frequency domain information of image defects after the wavelet transform processing, and simultaneously performing high-pass filtering processing on the image after the wavelet transform processing;
binarizing the filtered image according to a preset image pixel value threshold;
and counting the area of the defect area in the binarized image, if the area is larger than a preset area value threshold value, judging the image containing the defect, and if the area is smaller than the preset area value threshold value, judging the image not containing the defect.
Preferably, the third detecting unit 24 further includes:
determining the pyramid layer number, extracting the area of the defects in the pre-established defect image set X to form an area set M, squaring each element in the M to obtain a rectangular side length set L, and clustering the set L, wherein the number of classes is the pyramid layer number.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require a relatively clear and comprehensive understanding of the meaning or content of the features recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any fixed processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for detecting surface defects of strip steel is characterized by comprising the following steps:
acquiring a surface image of the strip steel;
performing defect detection on the surface image of the strip steel by adopting a rapid two-classification detection method, judging whether the surface image of the strip steel contains defects, if so, performing accurate detection and classification on the surface image of the strip steel containing the defects by adopting a defect detection method of a pyramid structure, and determining the positions and the types of the defects contained in the surface image of the strip steel;
if not, performing defect detection on the surface image of the strip steel by adopting an image processing method, determining whether the surface image of the strip steel contains defects, if so, performing accurate detection and classification on the surface image of the strip steel containing the defects by adopting a defect detection method of a pyramid structure, determining the positions and the types of the defects contained in the surface image of the strip steel, and if not, outputting the defect-free image.
2. The method for detecting the surface defects of the strip steel according to claim 1, wherein the rapid two-classification detection method comprises the following steps:
and carrying out edge detection on the surface image of the strip steel, carrying out binarization on the edge image of the surface image of the strip steel according to a preset edge pixel value threshold, and judging whether the surface image of the strip steel contains sharp defects or not according to the edge image after binarization.
3. The method for detecting the surface defects of the strip steel according to claim 1, wherein the image processing method comprises the following steps:
carrying out image noise reduction processing on the surface image of the strip steel;
carrying out image enhancement processing on the noise-reduced strip steel surface image;
performing wavelet transform processing on the enhanced image, extracting frequency domain information of image defects after the wavelet transform processing, and simultaneously performing high-pass filtering processing on the image after the wavelet transform processing;
binarizing the filtered image according to a preset image pixel value threshold;
and counting the area of the defect area in the binarized image, if the area is larger than a preset area value threshold value, judging the image containing the defect, and if the area is smaller than the preset area value threshold value, judging the image not containing the defect.
4. The method for detecting the surface defects of the strip steel according to claim 1, wherein the step of accurately detecting and classifying the surface images of the strip steel containing the defects by adopting a defect detection method of a pyramid structure further comprises the following steps of:
determining the pyramid layer number, extracting the area of the defects in the pre-established defect image set X to form an area set M, squaring each element in the M to obtain a rectangular side length set L, and clustering the set L, wherein the number of classes is the pyramid layer number.
5. The method for detecting the surface defects of the strip steel according to claim 1, wherein the step of accurately detecting and classifying the surface images of the strip steel containing the defects by adopting a defect detection method of a pyramid structure comprises the following steps of:
generating a characteristic diagram of the strip steel surface image in each layer of pyramid, proposing candidate areas in each layer of pyramid characteristic diagram, and after removing the overlapped candidate areas by using a non-maximum inhibition method, proposing n positions which are possibly defective areas by each layer of pyramid;
and putting the n-pyramid-layer defect candidate regions into a convolution network, and outputting the defect possibility, defect types and fine-tuned region positions of each candidate region.
6. The method for detecting the surface defects of the strip steel according to claim 2 or 3, wherein the binarization processing comprises:
and the binarization processing is to set the pixel value of the pixel point of the strip steel surface image, the pixel value of which is lower than a preset pixel value threshold value, as 0 and the pixel value of the pixel point of which is higher than the preset pixel value threshold value, as 255.
7. A strip steel surface defect detection device, characterized by includes:
the image acquisition unit is used for acquiring a surface image of the strip steel;
the first detection unit is used for detecting the defects of the surface image of the strip steel by adopting a rapid two-classification detection method and judging whether the surface image of the strip steel contains the defects or not;
the second detection unit is used for detecting the defects of the strip steel surface images of which the defects are not detected by the first detection unit by adopting an image processing method, and determining whether the strip steel surface images contain the defects;
and the third detection unit is used for accurately detecting and classifying the band steel surface images containing the defects determined by the first detection unit and the second detection unit by adopting a defect detection method of a pyramid structure, and determining the positions and the types of the defects contained in the band steel surface images.
8. The apparatus for detecting surface defects of a strip steel according to claim 7, wherein the first detecting unit comprises:
and carrying out edge detection on the surface image of the strip steel, carrying out binarization on the edge image of the surface image of the strip steel according to a preset edge pixel value threshold, and judging whether the surface image of the strip steel contains sharp defects or not according to the edge image after binarization.
9. The apparatus for detecting surface defects of strip steel according to claim 7, wherein the second detecting unit comprises:
carrying out image noise reduction processing on the surface image of the strip steel;
carrying out image enhancement processing on the noise-reduced strip steel surface image;
performing wavelet transform processing on the enhanced image, extracting frequency domain information of image defects after the wavelet transform processing, and simultaneously performing high-pass filtering processing on the image after the wavelet transform processing;
binarizing the filtered image according to a preset image pixel value threshold;
and counting the area of the defect area in the binarized image, if the area is larger than a preset area value threshold value, judging the image containing the defect, and if the area is smaller than the preset area value threshold value, judging the image not containing the defect.
10. The apparatus for detecting surface defects of strip steel according to claim 7, wherein the third detecting unit further comprises:
determining the pyramid layer number, extracting the area of the defects in the pre-established defect image set X to form an area set M, squaring each element in the M to obtain a rectangular side length set L, and clustering the set L, wherein the number of classes is the pyramid layer number.
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