CN112198170B - Detection method for identifying water drops in three-dimensional detection of outer surface of seamless steel tube - Google Patents
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Abstract
The invention relates to the technical field of three-dimensional detection of the outer surface of a steel pipe, in particular to a detection method for identifying water drops in the three-dimensional detection of the outer surface of a seamless steel pipe, which comprises data preprocessing, model training and online detection. In the invention, three steps of data preprocessing, model training and online detection are used for detecting the seamless steel pipe, in actual production, a three-dimensional laser sensor is used for splicing and converting the contour line of the pipe body into a depth image of the surface of the steel pipe to be detected, a water drop detection model is used for detecting the depth image, morphological operation and out-of-area searching are firstly carried out on the depth image to obtain an area to be detected which is suspected to be water drops, and then a convolutional neural network is used for identifying the area to be detected.
Description
Technical Field
The invention relates to the technical field of three-dimensional detection of the outer surface of a steel pipe, in particular to a detection method for identifying water drops in nondestructive detection of the outer surface of a seamless steel pipe.
Background
The hot rolled seamless steel pipe needs to be sprayed and cooled in the production process, and particularly needs to be sprayed with water as a coupling agent when passing through ultrasonic detection equipment, so that water drops sometimes exist on the surface of the pipe body. When the three-dimensional sensor is used for collecting the profile data of the pipe body, water drops are in the shape of pits, bulges, pits and bulges on the depth map, the shape is mainly provided with transverse stripes on two sides of an ellipse, the shapes of the pits and the bulges on the surfaces of different steel pipes with iron sheets, paint spraying and the like are changeable, the pits and the bulges are easily confused with real defects, serious interference is caused to detection, and frequent false alarms are caused to detection equipment. At present, a detection method for identifying water drops with high accuracy does not exist, so a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel pipe is provided for solving the problems.
Disclosure of Invention
The invention aims to provide a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel pipe, which uses three steps of data preprocessing, model training and online detection to detect a hot rolled steel pipe. In order to achieve the above purpose, the present invention provides the following technical solutions:
a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel tube comprises the following steps:
step 1: data preprocessing: generating a two-dimensional gray scale image of the surface of the pipe body according to the pipe body profile data obtained by the three-dimensional sensor, and preprocessing the gray scale image to obtain all areas to be detected;
step 2: model training: constructing a deep learning network suitable for detecting water drops on the surface of the steel pipe in an off-line manner, and training a model in the off-line manner by using the images of the areas to be detected collected in the step 1;
step 3: and (3) online detection: and (3) loading the model obtained in the step (2) into detection equipment, and obtaining all areas to be detected according to the step (1) when the detection equipment works, and detecting water drops on the surface of the pipe body on line.
Preferably, the data preprocessing section specifically includes:
1. when a steel pipe passes through the detection device, a three-dimensional sensor collects contour data of the surface of the steel pipe, three-dimensional point cloud data of the obtained contour are fitted to a reference surface of the steel pipe by using a least square method, points located on the surface of the steel pipe, points protruding from the surface of a pipe body and concave points are projected into different values respectively, and the three-dimensional point cloud data are converted into two-dimensional gray images;
2. because the three-dimensional sensor collects the surface profile data of the steel pipe, the image uploaded to the industrial personal computer is only a part of the steel pipe each time, water drops on the image can be cut and distributed on two images, and in order to ensure the detection accuracy, the rear half part of the previous image is spliced on the top of the current image;
3. performing open operation of corrosion and expansion on the gray level image by using 3 multiplied by 3 square structural elements, removing isolated noise points, and marking a non-zero area on the graph after removing the noise points as a suspected area region;
4. and extracting an edge contour edge of the suspected region, and carrying out region searching on points on the edge. Filtering the gray image by using a 3 x 3 matrix with a point P (x, y) on the edge profile as an origin, filtering out points in the region, searching out the region according to a formula (1), changing P1 (x, y) into a new origin when a pixel point P1 (x, y) which is different from the gray value of P (x, y) by 0.5 is searched out, continuing searching out the pixel point which is different from the gray value of P1 (x, y) by 0.5 out of the region until the point which does not meet the condition is searched out, and adding all the searched points meeting the condition into the original suspected region;
edge＝{...，P(x，y)，P 1 (x，y)|abs(P 1 (x, y) -P (x, y)) > 0.5} formula (2)
5. And merging the adjacent suspected areas to be detected. The merging method is to calculate the merging ratio of adjacent suspected areas, and the suspected areas with the merging ratio larger than 0.85 are areas to be detected. The calculation method of the intersection ratio IoU of the suspected region A and the suspected region B is as follows: the intersection of region a and region b is the proportion of the union. If the merging IoU of the region a and the region b is greater than 0.85, the region a and the region b are merged into the region to be detected region1, and region1 is the union of the region a and the region b.
Preferably, the model training part specifically includes:
1. constructing a deep learning network suitable for detecting water drops on the surface of the steel pipe, wherein the deep learning network comprises a convolutional neural network for extracting characteristics and a regional suggestion network for accurately generating targets;
2. taking the image of the area to be detected as a training image, and training a water drop detection model offline: and collecting more than 100 pictures with water drops and more than 100 pictures without water drops as samples, training by using a deep learning network until the loss function is less than 0.001, and obtaining a water drop detection model.
Preferably, the on-line detection part specifically includes:
1. when the steel pipe passes through the detection device, the three-dimensional sensor acquires the surface profile data of the steel pipe, and the three-dimensional point cloud data of the profile is obtained. Fitting the surface of the steel pipe by using a least square method, and projecting the contour data into a two-dimensional gray image;
2. the second half part of the previous frame of image is spliced at the top of the current image;
3. carrying out morphological operation on the image, and removing noise points to obtain a suspected region;
4. calculating to search out the area of the point at the edge of the suspected area, and expanding the suspected area;
5. combining two adjacent suspected areas to be detected;
6. and taking all the areas to be detected on the picture as input of a water drop detection model once, judging whether the areas to be detected are water or not, and displaying the identification result on a software interface.
The convolutional neural network is used for extracting characteristics, performs normalization processing on input image data, adjusts the image size to 224×224, and performs characteristic extraction on each image by using an 8-layer convolutional neural network, specifically: the first layer is a convolution layer, the convolution kernel is 11×11, the step length is 2, the second layer is a maximum pooling layer, the size is 3×3, the step length is 2, and the input of the second layer is the output of the first layer; the third layer is a convolution layer, the convolution kernel is 5 multiplied by 5, the step length is 2, the input of the third layer is the weighted sum of the output of the first layer and the output of the second layer, the fourth layer is a maximum pooling layer, the size is 3 multiplied by 3, the step length is 2, and the input of the fourth layer is the output of the third layer; the fifth layer is a convolution layer, the convolution kernel size is 3×3, the step length is 1, and the input of the fifth layer is the weighted sum of the output of the first layer, the output of the third layer and the output of the fourth layer; the sixth layer is a convolution layer, the convolution kernel size and the step length of the sixth layer are the same, and the input of the sixth layer is the weighted sum of the output of the first layer, the output of the third layer, the output of the fourth layer and the output of the fifth layer; the seventh layer is a convolution layer, the convolution kernel size and the step length of the seventh layer are the same, the input of the seventh layer is the weighted sum of the output of the first layer, the output of the third layer, the output of the fourth layer, the output of the fifth layer and the output of the sixth layer, the eighth layer is a maximum pooling layer, the size is 3 multiplied by 3, the step length is 2, and the input of the eighth layer is the output of the seventh layer, so that a characteristic diagram of an original graph is obtained.
The regional suggestion network for accurately generating the target consists of a regional pyramid network, a pooling layer and a full-connection layer, and specifically comprises the following steps: processing feature information extracted by a convolutional neural network by using a feature pyramid network FPN, integrating high-level features of low-resolution and high-semantic information and low-level features of high-resolution and low-semantic information, and obtaining a candidate region ROI; mapping a candidate region ROI obtained from the network of the previous layer to a corresponding position on the feature map by using an ROI pooling layer, dividing the mapped region into regions with the same size, and carrying out maximum pooling operation on each region to obtain a feature map with a fixed size; and classifying and predicting the output result of the pooling layer by using the full-connection layer to obtain the type of each pixel on the original picture, thereby realizing the detection of water drops.
Compared with the prior art, the invention has the beneficial effects that: the three steps of data preprocessing, model training and online detection are used for detecting the hot rolled steel pipe, in actual production, the three-dimensional laser sensor is used for splicing the contour line of the pipe body into an arc, the surface of the steel pipe is fitted by a least square method, contour data are projected into a two-dimensional gray image, the image is preprocessed, the region to be detected is obtained, the water drop detection model is used for detecting, detection errors are greatly reduced, and the method is suitable for popularization and use.
Drawings
FIG. 1 is a schematic diagram of the overall structure of a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel tube;
FIG. 2 is a schematic diagram of a feature extraction network in a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel tube;
fig. 3 is a schematic diagram of a water drop picture and a label in a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel tube.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 1-3, the present invention provides a technical solution:
a detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel tube comprises the following steps:
step 1: data preprocessing: generating a two-dimensional gray scale image of the surface of the pipe body according to the pipe body profile data obtained by the three-dimensional sensor, and preprocessing the gray scale image to obtain all areas to be detected;
step 2: model training: constructing a deep learning network suitable for detecting water drops on the surface of the steel pipe in an off-line manner, and training a model in the off-line manner by using the images of the areas to be detected collected in the step 1;
step 3: and (3) online detection: and (3) loading the model obtained in the step (2) into detection equipment, and obtaining all areas to be detected according to the step (1) when the detection equipment works, and detecting water drops on the surface of the pipe body on line.
The following operations are carried out according to the method:
training a water drop detection model offline: 230 pictures obtained by projection of the outline of the pipe body are collected, the positions of water drops 119 on the pictures are marked, 111 pictures without water drops are marked, the GPU used for training is 1080Ti, and the training is carried out for 12 hours.
And (3) detecting water drops on line: a three-dimensional laser sensor of the model gccator 2340 of the company lmi is selected to collect pictures, the contour lines of the pipe body are spliced into circular arcs, the surface of the steel pipe is fitted by a least square method, and the contour data are projected into a two-dimensional gray image.
The image is preprocessed to obtain an area to be detected, the area to be detected is detected by a water drop detection model, 223 pictures with water drops and 2675 pictures without water drops are acquired, 208 pictures are correctly detected by an algorithm, 15 pictures are missed to be detected, the false detection is carried out to form 0 pictures with water drops, the detection time of each frame of pictures is 1.5ms, and the actual use requirements are met.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. A detection method for identifying water drops in three-dimensional detection of the outer surface of a seamless steel tube comprises the following steps:
step 1: data preprocessing: generating a two-dimensional gray scale image of the surface of the pipe body according to the pipe body profile data obtained by the three-dimensional sensor, and preprocessing the gray scale image to obtain all areas to be detected;
step 2: model training: constructing a deep learning network suitable for detecting water drops on the surface of the steel pipe in an off-line manner, and training a model in the off-line manner by using the images of the areas to be detected collected in the step 1;
step 3: and (3) online detection: loading the model obtained in the step 2 into detection equipment, and obtaining all areas to be detected according to the step 1 when the detection equipment works, and detecting water drops on the surface of the pipe body on line;
the data preprocessing step specifically comprises the following steps:
1) When a steel pipe passes through the detection device, a three-dimensional sensor collects contour data of the surface of the steel pipe to obtain three-dimensional point cloud data of the contour, a least square method is used for fitting a reference surface of the steel pipe, points located on the surface of the steel pipe, points protruding from the surface of a pipe body and concave points are projected into different values respectively, and the three-dimensional point cloud data are converted into two-dimensional gray images;
2) Because the three-dimensional sensor collects the surface profile data of the steel pipe, the image uploaded to the industrial personal computer is only a part of the steel pipe each time, water drops on the image can be cut and distributed on two images, and in order to ensure the detection accuracy, the rear half part of the previous image is spliced on the top of the current image;
3) Performing open operation of corrosion and expansion on the gray level image by using 3 multiplied by 3 square structural elements, removing isolated noise points, and marking a non-zero area on the graph after removing the noise points as a suspected area region;
4) Extracting edge contour edge of suspected region, performing out-of-region search on points on edge, processing gray image with 3×3 matrix with point P (x, y) on edge contour as origin, filtering out points in region, searching out region according to 3×3 matrix, and when P (x, when the gray value of y) is different from the gray value of P1 (x, y) of 0.5, changing P1 (x, y) into a new origin, continuing to search the pixel which is different from the gray value of P1 (x, y) by 0.5 from outside the region until the point which meets the condition is not searched, and adding all the searched points which meet the condition into the original suspected region;
5) The adjacent suspected areas are merged to be the areas to be detected, the merging method is to calculate the merging ratio of the adjacent suspected areas, the suspected areas with the merging ratio larger than 0.85 are taken as the areas to be detected, and the calculating mode of the merging ratio IoU of the suspected areas regionA and regionB is as follows: if the merging IoU of the region a and the region b is greater than 0.85, merging the region a and the region b into a region to be detected region1, wherein region1 is the union of the region a and the region b;
the model training steps are as follows:
1) Constructing a deep learning network suitable for detecting water drops on the surface of the steel pipe, wherein the deep learning network comprises a convolutional neural network for extracting characteristics and a regional suggestion network for accurately generating targets;
2) Taking the image of the area to be detected as a training image, and training a water drop detection model offline: collecting more than 100 pictures with water drops and more than 100 pictures without water drops as samples, training by using a deep learning network until the loss function is less than 0.001, and obtaining a water drop detection model;
the online detection step specifically comprises the following steps:
1) When the steel pipe passes through the detection device, the three-dimensional sensor collects contour data of the surface of the steel pipe, three-dimensional point cloud data of the contour are obtained, the surface of the steel pipe is fitted by using a least square method, and the contour data are projected into a two-dimensional gray image;
2) The second half part of the previous frame of image is spliced at the top of the current image;
3) Carrying out morphological operation on the image, and removing noise points to obtain a suspected region;
4) Performing out-of-area search on points at the edge of the suspected area, and expanding the suspected area;
5) Combining two adjacent suspected areas as areas to be detected;
6) And sequentially taking all the areas to be detected on the picture as the input of a water drop detection model, judging whether the areas to be detected are water drops or not, and displaying the identification result on a software interface.
2. The detection method for identifying water drops in the three-dimensional detection of the outer surface of the seamless steel tube according to claim 1, which is characterized in that: the convolutional neural network is used for extracting features, performs normalization processing on input image data, adjusts the image size to 224×224, and performs feature extraction on each image by using an 8-layer convolutional neural network, specifically: the first layer is a convolution layer, the convolution kernel is 11×11, the step length is 2, the second layer is a maximum pooling layer, the size is 3×3, the step length is 2, and the input of the second layer is the output of the first layer; the third layer is a convolution layer, the convolution kernel is 5 multiplied by 5, the step length is 2, the input of the third layer is the weighted sum of the output of the first layer and the output of the second layer, the fourth layer is a maximum pooling layer, the size is 3 multiplied by 3, the step length is 2, and the input of the fourth layer is the output of the third layer; the fifth layer is a convolution layer, the convolution kernel size is 3×3, the step length is 1, and the input of the fifth layer is the weighted sum of the output of the first layer, the output of the third layer and the output of the fourth layer; the sixth layer is a convolution layer, the convolution kernel size and the step length of the sixth layer are the same, and the input of the sixth layer is the weighted sum of the output of the first layer, the output of the third layer, the output of the fourth layer and the output of the fifth layer; the seventh layer is a convolution layer, the convolution kernel size and the step length of the seventh layer are the same, the input of the seventh layer is the weighted sum of the output of the first layer, the output of the third layer, the output of the fourth layer, the output of the fifth layer and the output of the sixth layer, the eighth layer is a maximum pooling layer, the size is 3 multiplied by 3, the step length is 2, and the input of the eighth layer is the output of the seventh layer, so that a characteristic diagram of an original graph is obtained.
3. The detection method for identifying water drops in the three-dimensional detection of the outer surface of the seamless steel tube according to claim 1, which is characterized in that: the regional suggestion network for accurately generating the target consists of a regional pyramid network, a pooling layer and a full-connection layer, and specifically comprises the following steps: processing feature information extracted by a convolutional neural network by using a feature pyramid network FPN, integrating high-level features of low-resolution and high-semantic information and low-level features of high-resolution and low-semantic information, and obtaining a candidate region ROI; mapping a candidate region ROI obtained from the network of the previous layer to a corresponding position on the feature map by using an ROI pooling layer, dividing the mapped region into regions with the same size, and carrying out maximum pooling operation on each region to obtain a feature map with a fixed size; and classifying and predicting the output result of the pooling layer by using the full-connection layer to obtain the type of each pixel on the original picture, thereby realizing the detection of water drops.
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