CN113240663A - Conveyor belt ore granularity detection method based on edge response fusion algorithm - Google Patents
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Abstract
The invention relates to a conveyor belt ore granularity detection method based on an edge response fusion algorithm, which comprises the following steps: (1) shooting an ore image on a conveyor belt in real time by using an industrial camera; (2) preprocessing acquired images by image graying, denoising and image binarization; (3) optimizing the binary image and transforming the distance by using a morphological method; (4) an edge response fusion algorithm combining improved HED network edge detection and a watershed algorithm based on distance transformation is adopted to effectively segment the ore image; (5) calling a part of functions in an OpenCV library to perform granularity analysis calculation on the segmented image; (6) and transmitting the ore granularity information to a crusher control system in real time. The accurate ore granularity obtained by calculation can provide important reference for adjusting related parameters of the crusher, reduce the fault occurrence probability of the ore crusher and improve the production efficiency of mines.
Description
Technical Field
The invention belongs to the technical field of mining, simultaneously belongs to the fields of computer application and deep learning, relates to automatic detection of ore granularity, and particularly relates to a conveyor belt ore granularity detection method based on an edge response fusion algorithm.
Background
In the mining industry, the crushing effect of ores in a crushing station can directly reflect the blasting efficiency. The ore after the explosive blasting is loaded to a truck and then is transported to a crushing station to complete 2 times of crushing, the work is mainly completed by a crusher, and the crushed ore is transported to other stations by a conveyor belt after being crushed by the crusher. The adjustment of the parameters of the crusher is closely related to the size of the ore granularity, and how to determine the size of the ore granularity on the crushed conveying belt so as to properly adjust the parameters of the crusher has important significance for mine production. As a traditional method, manual screening is used in mining areas for many years, so that the efficiency is low, the mining cost is invisibly increased, and the method is not suitable for intelligent development of mines. At present, a plurality of ore granularity detection methods based on vision and image processing technologies are applied to part of mine development so as to adjust the parameters of the crusher to be optimal in time, reduce energy consumption and save cost; bringing considerable economic benefit for mine production.
The use of computer vision and image processing techniques to determine the particle size of ore on a conveyor belt also presents a number of problems in the application process. Due to the complex mine environment, the illumination can not meet the ideal requirement, so that the quality of the obtained ore picture is low and the obtained ore picture is full of noise; and the ore granularity on the conveyer belt is different, and different ores are adhered together, and these all bring huge difficulty to image segmentation. The conventional algorithm based on threshold segmentation has difficulty in obtaining the optimal threshold, which often results in the failure to segment the image to the ideal effect. The watershed algorithm is a classic segmentation algorithm, the problem of adhesion of ores cannot be effectively solved, and over-segmentation and under-segmentation phenomena are easily caused.
Disclosure of Invention
In order to overcome the defects of the prior art and solve the problem of inaccuracy of the existing ore image segmentation algorithm, the invention aims to provide the conveyor belt ore granularity detection method based on the edge response fusion algorithm, the deep learning and the traditional method are combined, the two segmentation results are subjected to one AND operation to exert respective advantages, the respective vacancy of edge detection is filled, the segmentation effect is optimized, and the granularity information of ore is obtained through accurate image segmentation, so that the parameters of a crusher are adjusted, the fault rate of the crusher is reduced, the production efficiency of a mine is improved, and the mining cost is saved.
In order to achieve the purpose, the invention adopts the technical scheme that:
a conveyor belt ore granularity detection method based on an edge response fusion algorithm comprises the following steps:
step (1), using an industrial camera to shoot an ore image on a conveyor belt in real time;
step (2), preprocessing the collected image, including image graying, denoising and image binarization;
step (3), carrying out binarization image optimization and distance transformation on the preprocessed image;
step (4), an edge response fusion algorithm combining improved HED network edge detection and a watershed algorithm based on distance transformation is adopted to effectively segment the ore image;
and (5) calculating the granularity characteristic parameters of the ore by using the segmented image to obtain the granularity information of the ore.
Compared with the prior art, the conveyor belt ore granularity detection method based on the edge response fusion algorithm combines the improved HED edge detection method with the watershed algorithm based on distance transformation to obtain a more accurate conveyor belt ore image segmentation map, further obtains the granularity information of the ore, feeds the granularity information back to a control system of a crusher, and adjusts the crushing aperture of the crusher, the speed of the ore entering the crusher, the speed of a rotor and other factors which easily cause serious faults of the crusher. Accurate ore granularity information is obtained through calculating a segmentation graph, so that the fault rate of a crusher is greatly reduced, the production efficiency of a mine is improved, and the mining cost is saved.
Drawings
FIG. 1 is a flow chart of an implementation of the conveyor belt ore granularity detection method based on the edge response fusion algorithm.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
As shown in fig. 1, the invention relates to a conveyor belt ore granularity detection method based on an edge response fusion algorithm, which is based on a conveyor belt ore image granularity detection algorithm of a semantic segmentation technology, combines an improved HED network edge detection algorithm with a watershed algorithm based on distance transformation to obtain a segmentation image of an ore, and calls a partial function of OpenCV to obtain the granularity of the ore.
The method comprises the following specific steps:
and (1) installing an industrial camera on the conveyor belt, shooting an ore image on the conveyor belt in real time by using the industrial camera, transmitting the image into a background system, and generally randomly grabbing and shooting.
In order to obtain better segmentation effect, a conveyor belt ore photo with better quality needs to be taken firstly, the good photo can enable the pre-processed effect to be better, and the influence of external factors on the undesirable segmentation is reduced.
And (2) preprocessing the acquired image, including image graying, denoising and image binarization. Specifically, the method comprises the following steps:
a. the graying process is to adjust the gray value of each pixel point of the image to the average value of RGB components. That is, the average value of RGB of each pixel point of the ore image is obtained, so that the RGB values are equal, the image enhancement effect is achieved, and useful image information is enhanced.
b. Because the mine environment is severe, collected conveyor belt ore images are often full of noise, so that the segmentation effect in the later period is not ideal, and in order to solve the problem, the images are denoised by adopting a filtering method. The invention adopts a bilateral filtering method to carry out denoising, determines the value of an output pixel through the weighted combination of the pixel values of the neighborhood, and can achieve the best denoising effect while preserving the ore edge.
Bilateral filtering is a filtering method based on Gauss and has strong practicability. The specific method is that the brightness information of the image and the Gauss function are multiplied to optimize the filtering weight coefficient, and then the obtained coefficient and the image are subjected to convolution operation, so that the edge information of the image can be retained to the maximum extent. The denoising effect of bilateral filtering depends on the spatial distance and the pixel difference, and the weighted combination of the neighboring pixels can be represented by the following formula:
the weighting factor w (i, j, k, l) is determined by the product of the domain-defining kernel d (i, j, k, l) and the value domain kernel r (i, j, k, l).
σdAnd σrThe method is based on the standard deviation of a Gaussian function, is known as a spatial proximity factor and a brightness proximity factor, and can be used for controlling the performance of the bilateral filter. The weighted average value is used for replacing the gray value of the pixel, and the method can well represent that the gray value difference is smallThe pixel is suitable for different ore particles with similar colors. SigmadAnd σrIs a determining factor influencing the denoising effect, and therefore, how to take the appropriate value for the two is a key step of bilateral filtering. After a plurality of experiments, finally taking sigmad＝30，σrThe filtering effect is optimal when the value is 0.05.
c. The image is binarized by using an integral image-based adaptive thresholding algorithm, the image is thresholded mainly by using an integral image method, and the finally obtained binarized image H (n) can be represented by the following formula:
the value of t in the formula is 15, and the mean value of the gray scale of the pixel point in the rectangular frame determines the threshold value of binarization, wherein the mean value of the gray scale (x, y) is determined by the following formula:
and (3) carrying out binarization image optimization and distance transformation on the preprocessed image. Specifically, the method comprises the following steps:
the method of the integral image is used for obtaining the binary image, and because the shape of the ore stone is extremely irregular, isolated points such as holes, burrs and the like still exist in the binary image, which can cause the result of segmentation to be inaccurate. The method comprises the following steps of optimizing a binary image by using a mathematical morphology method, carrying out expansion and corrosion operations on the image, and removing 'holes' and isolated noise points in the image, wherein the method mainly comprises the following steps:
the first step is as follows: and setting a structural element value for the image, and performing simple opening and closing operation on the image to reduce a small part of obvious holes and noise points.
The second step is that: and obtaining a complementary image of the original image by using an erosion reconstruction method.
The third step: and optimizing the complementary image, and then performing open operation to obtain a final optimized image.
Experiments prove that the binary image can be effectively optimized by using a mathematical morphology method, so that the edge part of the ore is smoother, and the expansion operation can effectively fill part of holes, so that the segmentation result is more accurate.
And then, performing distance transformation on the binary image, namely processing pixel points in the neighborhood of the image, and reducing over-segmentation.
And (4) adopting an edge response fusion algorithm combining improved HED network edge detection and a watershed algorithm based on distance transformation to perform real-time edge detection, and effectively segmenting the ore image. Specifically, the method comprises the following steps:
the improved HED network used by the invention is improved in a small part on the basis of the original network. Because the original HED network feature extraction layer is mainly based on the VGG network and includes a large number of pooling layers, the pooling layers can increase the receptive field while extracting image features, but reduce the size of the feature map, resulting in reduced resolution. Meanwhile, some edge information in the ore feature map is lost, which affects the segmentation accuracy.
The improved HED network deletes the third and fourth pooling layers, so that more network high-level image information with richer semantic features can be extracted, the extracted edges are finer, and the problems of over-segmentation and under-segmentation caused by ore adhesion can be well solved by combining a watershed algorithm based on distance transformation. Meanwhile, the deconvolution layer on the side edge is correspondingly modified; and replacing the remaining pooling layer with a hole convolution to increase the receptive field. The two-step improved operation not only can enable the extracted ore edge to be finer, but also reduces the workload of parameter design, obtains more ore image global information and provides an effective edge for subsequent edge fusion.
The expression for the hole convolution can be represented by:
n＝k+(k-1)×(d-1)
wherein k is the size of the convolution kernel; p is the padding number in the convolution process; s is the convolution step length; d is the void convolution sampling rate; i is the size of the input feature map, and n and o are the size of a new convolution kernel and the size of an output feature map after the void convolution respectively;
the loss function of the improved HED network is:
wherein L isside(W, W) is the output cost of the edge, αmFor the weight of the loss function for each side,
for the loss function of each side, W is the set of all parameters in the network, W is the weight parameter in the side output network, m is the current layer number, n is the total layer number, W(m)Is the network weight parameter output by the mth layer side.
The network optimizes each iteration by using a cross entropy function, and the expression is as follows:
(W,w,h)*＝argmin(Lside(W,w)+Lfuse(W,w，h))
after a series of preprocessing, labeling the images by using labelme software, expanding the number of samples by a series of data enhancement operations such as cutting, reversing and zooming of the labeled images, finally dividing the data set into a training set and a testing set according to the proportion of 8:2, uniformly adjusting the size of pixels to 512x512, training 5000 times in the environment of 4 RTX2080 video cards with a programming language of Python and OpenCV of Python3.5, iterating once every 100 times, and setting the batch _ size to be 1. And reserving the optimal training weight, testing the test set and reserving the result.
Because conveyor belt ore is heavily stacked and ores of different sizes are stuck together, it is difficult to accurately segment the ore by one segmentation method alone, and there are severe over-segmentation and under-segmentation, so that the results obtained by the two methods are considered to be fused.
The improved HED network would fail to segment the stuck particles where the ore stacking is severe, while the watershed algorithm excels in segmenting the stuck particles, just as well as complementing them. The invention adopts a watershed algorithm of distance transformation to segment the adhered particles, and the essence of the method is that the distance transformation is matched with the watershed transformation, so that the direct watershed segmentation of the ore image is easy to generate over-segmentation. And the distance transformation is carried out on the obtained binary image, so that the over-segmentation can be effectively reduced. It mainly acts on the pixels in the neighborhood, and there are two general measurement methods, four neighborhood d(4)And eight neighborhoods d(8). The minimum proximity distance from a pixel background point p (j, k) to the foreground pixel point p (l, m) is calculated as follows:
d(4)(l,m)＝min(|l-j|+|m-k|)
d(8)(l，m)＝min{max(|l-j|)，|m-k|}
l, m, j, k are coordinate values, d(4)(l, m) denotes the four neighbourhoods d(4)Minimum proximity distance, d, calculated by a metric method(8)(l, m) denotes the eight neighbourhood d(8)The minimum proximity distance calculated by the metric method.
And (3) applying a watershed algorithm to the distance-transformed binary image to obtain an ore segmentation image, fusing the ore segmentation image with the ore edge segmentation image obtained by adopting the improved HED network, wherein the fusion of the images is to make an AND algorithm for two results, and effectively merging the two segmentation images to obtain the final segmentation image. The watershed algorithm can effectively make up for the deficiency of the segmentation capability of the HED network at the adhesion part, and the HED network can make up for the characteristic that the design of watershed parameters is too complex.
And (5) analyzing and calculating the granularity characteristic parameters of the ore by using the segmented image to obtain the granularity information of the ore. Specifically, the method comprises the following steps:
and calling an OpenCV library, and calculating characteristic information such as the area, the perimeter, the total amount and the like of the ore particles. Finding all ore particle contours in the segmentation map by using a findContours algorithm; obtaining the minimum external rectangle of each ore outline by using a boundingRec algorithm; calculating the area of each contour by using a contourArea algorithm; the perimeter of each contour is calculated using the arcLength algorithm.
On this basis, can still further include:
and (6) transmitting the obtained ore granularity information to a crusher control system in real time, and adjusting parameters which can cause the crusher to break down, such as crushing caliber, feeding and discharging speed and the like according to the granularity.
In summary, the invention obtains the high-precision conveyor belt ore segmentation image through the fusion algorithm, and calculates the precise ore granularity according to the image, thereby providing important reference for adjusting the relevant parameters of the crusher, reducing the fault occurrence probability of the ore crusher, improving the production efficiency of the mine and saving the mining cost.
Claims (10)
1. A conveyor belt ore granularity detection method based on an edge response fusion algorithm is characterized by comprising the following steps:
step (1), using an industrial camera to shoot an ore image on a conveyor belt in real time;
step (2), preprocessing the collected image, including image graying, denoising and image binarization;
step (3), carrying out binarization image optimization and distance transformation on the preprocessed image;
step (4), an edge response fusion algorithm combining improved HED network edge detection and a watershed algorithm based on distance transformation is adopted to effectively segment the ore image;
and (5) calculating the granularity characteristic parameters of the ore by using the segmented image to obtain the granularity information of the ore.
2. The conveyor belt ore granularity detection method based on the edge response fusion algorithm as claimed in claim 1, wherein in the step (2), the graying of the image is to adjust the gray value of each pixel point of the image to the average value of RGB components; denoising, namely performing denoising processing on an image by using bilateral filtering, and determining the value of an output pixel through weighted combination of pixel values of neighborhoods; image binarization is realized by using an adaptive thresholding algorithm based on an integral image.
3. The conveyor belt ore granularity detection method based on the edge response fusion algorithm as claimed in claim 1 or 2, wherein in the step (3), the binarized image is optimized by using a morphological method, and the binarized image is subjected to distance transformation.
4. The conveyor belt ore granularity detection method based on the edge response fusion algorithm as claimed in claim 3, wherein the morphological method is to perform expansion and corrosion operations on the image to remove 'holes' and isolated noise points in the image, so that the image edge is smoother, the distance is converted into pixel points in the neighborhood of the processed image, and over-segmentation is reduced.
5. The conveyor belt ore granularity detection method based on the edge response fusion algorithm of claim 1, wherein the improved HED network is obtained by reducing two pooling layers on the basis of an HED original network, correspondingly modifying the deconvolution layers on the side edges, and replacing the remaining pooling layers with hole convolution to increase the receptive field.
6. The conveyor belt ore granularity detection method based on the edge response fusion algorithm is characterized in that the expression of the void convolution is as follows:
n＝k+(k-1)×(d-1)
wherein k is the size of the convolution kernel; p is the padding number in the convolution process; s is the convolution step length; d is the void convolution sampling rate; i is the size of the input feature map, and n and o are the size of a new convolution kernel and the size of an output feature map after the void convolution respectively;
the loss function of the improved HED network is:
wherein L isside(W, W) is the output cost of the edge, αmFor the weight of the loss function for each side,
for the loss function of each side, W is the set of all parameters in the network, W is the weight parameter in the side output network, m is the current layer number, n is the total layer number, W(m)Is the network weight parameter output by the mth layer side.
7. The conveyor belt ore granularity detection method based on the edge response fusion algorithm as claimed in claim 1, 5 or 6, wherein the distance-transformed binarized image is subjected to a watershed algorithm to obtain an ore segmentation image, and the ore segmentation image is fused with an ore edge segmentation image obtained by using an improved HED network to obtain a final segmentation image, wherein the fusion of the images is to perform an AND algorithm on two results.
8. The conveyor belt ore granularity detection method based on the edge response fusion algorithm of claim 7, wherein in the watershed algorithm, the minimum proximity distance from a pixel background point p (j, k) to a foreground pixel point p (l, m) is calculated as follows:
d(4)(l，m)＝min(|l-j|+|m-k|)
d(8)(l，m)＝min{max(|l-j|)，|m-k|}
l, m, j, k are coordinate values, d(4)(l, m) denotes the four neighbourhoods d(4)Minimum proximity distance, d, calculated by a metric method(8)(l, m) denotes the eight neighbourhood d(8)The minimum proximity distance calculated by the metric method.
9. The conveyor belt ore granularity detection method based on the edge response fusion algorithm is characterized in that in the step (5), an OpenCV library is called, and all ore particle contours in a segmentation map are searched by using a findContours algorithm; obtaining the minimum external rectangle of each ore outline by using a boundingRec algorithm; calculating the area of each contour by using a contourArea algorithm; the perimeter of each contour is calculated using the arcLength algorithm.
10. The conveyor belt ore granularity detection method based on the edge response fusion algorithm as claimed in claim 1, wherein after the step (5), the obtained ore granularity information is transmitted to a crusher control system in real time, and parameters which can cause the crusher to malfunction are adjusted according to the granularity.
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