CN113139451A - Abnormal information generation method and device, electronic equipment and computer readable medium - Google Patents
Abnormal information generation method and device, electronic equipment and computer readable medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses an abnormal information generation method, an abnormal information generation device, an electronic device and a medium. One embodiment of the method comprises: extracting an image sequence and a target image from a target video, wherein the target image is an image of an article to be transported on a conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt; extracting the edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image; generating a statistical graph representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image; and sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart. The abnormal condition of this conveyer belt in the transportation object process can be fast, the efficient determined to this embodiment.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to an abnormal information generation method, an abnormal information generation device, electronic equipment and a computer readable medium.
Background
At present, more industries need to use conveying belts to transport objects. Various anomalies may occur during the transport of objects by the conveyor belt. Such as object jamming, conveyor belt breakage, etc. For detecting the abnormal condition in the process of conveying objects by the conveyor belt, the following methods are generally adopted: abnormal information in the conveying belt transportation process is generated by using a machine learning model such as a neural network.
However, when the above-mentioned abnormal information is generated in the above-mentioned manner, there are often the following technical problems:
machine learning algorithm detection and identification based on machine vision both require a large amount of labeling and training, and meanwhile, the calculation complexity is high, and the generalization capability is limited. The generation of abnormal information of the conveyor belt is not efficient and rapid enough.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an anomaly information generation method, apparatus, electronic device, and computer readable medium to solve one of the technical problems mentioned in the above background section.
In a first aspect, some embodiments of the present disclosure provide an exception information generating method, including: extracting an image sequence and a target image from a target video, wherein the target image is an image of an article to be transported on a conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt; extracting the edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image; generating a statistical graph representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image; and sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
Optionally, the generating a statistical graph representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image includes: performing a first pooling operation on each edge image in the edge image sequence to generate a first pooled image, so as to obtain a first pooled image sequence; performing a second pooling operation on the edge image corresponding to the target image to generate a second pooled image; and generating the statistical map according to the first pooling image sequence and the second pooling image.
Optionally, the generating the statistical map according to the first pooled image sequence and the second pooled image sequence includes: adding the pixel value sets of each line in the second pooled image to obtain a first vector; multiplying each first pooled image in the first pooled image sequence by corresponding pixels to obtain a multiplied image; performing evolution processing on each pixel in the multiplied image according to the number of first pooled images in the first pooled image sequence to obtain a processed image; adding the pixel value sets of each line in the processed image to obtain a second vector, wherein the data dimension of the second vector is the same as that of the first vector; and generating the statistical chart according to the first vector and the second vector.
Optionally, the generating the statistical graph according to the first vector and the second vector includes: subtracting the first vector from the second vector to obtain a third vector; inputting the third vector to a target activation function to obtain an output result; carrying out moving average processing on the output result to obtain a processing result; and visualizing the processing result to obtain the statistical chart.
Optionally, the sequentially generating the abnormal information of the conveyor belt according to a preset order in the statistical chart includes: sequentially determining a coordinate point set with a vertical coordinate value larger than a first threshold value according to the direction of the horizontal axis in the statistical chart; for each coordinate point in the set of coordinate points, performing the following processing steps to generate abnormal information corresponding to the coordinate point: determining a set of ordinate values of at least one coordinate point within a predetermined interval centered on an abscissa of the coordinate point; determining whether the mean value corresponding to the longitudinal coordinate value set is greater than a second threshold value; in response to determining that the average value is greater than the second threshold value, determining a location of an item jam on the conveyor belt corresponding to the coordinate point; and generating the abnormal information of the transmission belt according to the article jam position.
Optionally, the method further includes: in response to the average value being less than or equal to the preset threshold value, determining an article shielding position on the conveyor belt corresponding to the coordinate point; and generating the abnormal information of the transmission belt according to the article shielding position.
Optionally, the extracting edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image includes: carrying out image correction on each image in the image sequence to generate a first corrected image, and obtaining a first corrected image sequence; performing image correction on the target image to generate a second corrected image; performing noise reduction processing on each first correction image in the first correction image sequence to generate a first noise reduction image, so as to obtain a first noise reduction image sequence; performing noise reduction processing on the second correction image to generate a second noise reduction image; and extracting the edge information of the first noise-reduced image sequence and the second noise-reduced image by utilizing an edge detection algorithm and/or a contour detection algorithm to obtain edge images corresponding to the edge image sequence and the target image.
In a second aspect, some embodiments of the present disclosure provide an abnormality information generation apparatus, including: the extraction unit is configured to extract an image sequence and a target image from a target video, wherein the target image is an image of an article to be transported which does not exist on a conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt; an extracting unit configured to extract edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image; a first generating unit configured to generate a statistical chart representing the abnormal condition of the conveyor belt according to the edge image corresponding to the edge image sequence and the target image; and a second generation unit configured to sequentially generate the respective abnormality information of the conveyor belt in accordance with a predetermined order in the statistical chart.
Optionally, the first generating unit is configured to: performing a first pooling operation on each edge image in the edge image sequence to generate a first pooled image, so as to obtain a first pooled image sequence; performing a second pooling operation on the edge image corresponding to the target image to generate a second pooled image; and generating the statistical map according to the first pooling image sequence and the second pooling image.
Optionally, the first generating unit is configured to: adding the pixel value sets of each line in the second pooled image to obtain a first vector; multiplying each first pooled image in the first pooled image sequence by corresponding pixels to obtain a multiplied image; performing evolution processing on each pixel in the multiplied image according to the number of first pooled images in the first pooled image sequence to obtain a processed image; adding the pixel value sets of each line in the processed image to obtain a second vector, wherein the data dimension of the second vector is the same as that of the first vector; and generating the statistical chart according to the first vector and the second vector.
Optionally, the first generating unit is configured to: subtracting the first vector from the second vector to obtain a third vector; inputting the third vector to a target activation function to obtain an output result; carrying out moving average processing on the output result to obtain a processing result; and visualizing the processing result to obtain the statistical chart.
Optionally, the second generating unit is configured to: sequentially determining a coordinate point set with a vertical coordinate value larger than a first threshold value according to the direction of the horizontal axis in the statistical chart; for each coordinate point in the set of coordinate points, performing the following processing steps to generate abnormal information corresponding to the coordinate point: determining a set of ordinate values of at least one coordinate point within a predetermined interval centered on an abscissa of the coordinate point; determining whether the mean value corresponding to the longitudinal coordinate value set is greater than a second threshold value; in response to determining that the average value is greater than the second threshold value, determining a location of an item jam on the conveyor belt corresponding to the coordinate point; and generating the abnormal information of the transmission belt according to the article jam position.
Optionally, the second generating unit is configured to: determining an article shielding position on the conveyor belt corresponding to the coordinate point in response to the average value being less than or equal to the second threshold value; and generating the abnormal information of the transmission belt according to the article shielding position.
Optionally, the extraction unit is configured to: carrying out image correction on each image in the image sequence to generate a first corrected image, and obtaining a first corrected image sequence; performing image correction on the target image to generate a second corrected image; performing noise reduction processing on each first correction image in the first correction image sequence to generate a first noise reduction image, so as to obtain a first noise reduction image sequence; performing noise reduction processing on the second correction image to generate a second noise reduction image; and extracting the edge information of the first noise-reduced image sequence and the second noise-reduced image by utilizing an edge detection algorithm and/or a contour detection algorithm to obtain edge images corresponding to the edge image sequence and the target image.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program when executed by a processor implements a method as described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following beneficial effects: according to the abnormal information generation method of some embodiments of the disclosure, the abnormal condition of the conveyor belt in the process of transporting the object can be quickly and efficiently determined. Particularly, machine learning algorithm detection and recognition based on machine vision require a large amount of labeling and training, and meanwhile, the calculation complexity is high, and the generalization capability is limited. The generation of abnormal information of the conveyor belt is not efficient and rapid enough. Based on this, the anomaly information generation method of some embodiments of the present disclosure may first extract the image sequence and the target image from the target video for subsequent data support as anomaly information generation. Then, the edge information of the image sequence and the target image is extracted, and an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image are obtained. Here, the edge image sequence corresponding to the image sequence and the edge image corresponding to the target image are one of very important image features. The edge image retains the important partial image information in the original image, and the abnormal information of the conveyor belt can be effectively generated by analyzing the image information. And generating a statistical chart representing the abnormal condition of the conveyor belt according to the edge image corresponding to the edge image sequence and the target image. The abnormal information of the conveyor belt can be displayed more simply, conveniently and intuitively through the generated statistical chart. And finally, sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart. In a word, the abnormal information generation method can quickly and efficiently determine the abnormal condition of the conveyor belt in the process of transporting the object. And a large amount of labels and training are not needed, and meanwhile, the calculation complexity is low and the generalization capability is strong.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of one application scenario of an anomaly information generation method according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an exception information generation method according to the present disclosure;
FIG. 3 is a schematic illustration of image correction in some embodiments of an anomaly information generation method according to the present disclosure;
FIG. 4 is a schematic illustration of edge image generation in some embodiments of an anomaly information generation method according to the present disclosure;
FIG. 5 is a schematic illustration of determining an item congestion location in some embodiments of an anomaly information generation method according to the present disclosure;
FIG. 6 is a flow diagram of further embodiments of an exception information generation method according to the present disclosure;
FIG. 7 is a schematic block diagram of some embodiments of an anomaly information generating apparatus according to the present disclosure;
FIG. 8 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of an anomaly information generation method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, the electronic device 101 may first extract the image sequence 102 and the target image 103 from the target video. The target image 103 is an image of an article to be transported on a conveyor belt, and the image in the image sequence 102 is an image of an article transported by the conveyor belt. In the present application scenario, the image sequence 102 includes: image 1021, image 1022, and image 1023. In the target video, the time interval between the image 1021 and the image 1022 and the time interval between the image 1022 and the image 1023 may be the same. The time corresponding to the image 1021 is earlier than that of the image 1022. The image 1022 corresponds to a time earlier than the image 1023. Then, the edge information of the image sequence 102 and the target image 103 is extracted to obtain an edge image sequence 104 corresponding to the image sequence 102 and an edge image 105 corresponding to the target image 103. In the application scenario, the edge image sequence 104 may include: edge image 1041, edge image 1042, edge image 1043. Further, a statistical map 106 representing the abnormal situation of the conveyor belt is generated from the edge image 5 corresponding to the edge image sequence 104 and the target image 103. Finally, the abnormality information 107 of the conveyor belt is sequentially generated in accordance with the order set in advance in the statistical chart 106. In the application scenario, each of the above exception information 107 may include: exception information 1071, exception information 1072, and exception information 1073. As an example, the abnormality information 1071 may be a jam of an article. The anomaly 1072 may be a partial break in the conveyor belt. The abnormality information 1073 may be that the conveyor belt has an article adhered thereto.
The electronic device 101 may be hardware or software. When the electronic device is hardware, the electronic device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it may be installed in the above-listed hardware devices. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of electronic devices in fig. 1 is merely illustrative. There may be any number of electronic devices, as desired for implementation.
With continued reference to fig. 2, a flow 200 of some embodiments of an anomaly information generation method according to the present disclosure is shown. The abnormal information generation method comprises the following steps:
In some embodiments, an execution subject (e.g., the electronic device shown in fig. 1) of the above-described abnormality information generation method may extract the image sequence and the target image from the target video. The target image is an image of an article to be transported which does not exist on the conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt. The target video is composed of a plurality of frames of images. The target video may be a video for monitoring the transportation of each article by the conveyor belt. As an example, the image sequence may be obtained by randomly extracting several consecutive frames of images in the target video. In the multi-frame images, an image of a frame of conveyor belt without the article to be transported can be randomly extracted as a target image.
In some embodiments, the execution subject may extract edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image. The edge image may be an image obtained by performing edge extraction on an original image. The edge is a junction between the image region and another attribute region, is a place where the region attribute changes suddenly, is a place with the largest uncertainty in the image, and is also a place where the image information is most concentrated, and the edge of the image contains abundant information.
For example, the execution body may extract edge information of the image sequence and the target image by using a laplacian gaussian algorithm, and obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image.
As an example, the executing entity may use a computer vision library (OpenCV) to call a relevant interface to extract edge information of the image sequence and the target image, so as to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image.
In some optional implementation manners of some embodiments, the extracting edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image may include the following steps:
the method comprises the following steps of firstly, carrying out image correction on each image in the image sequence to generate a first corrected image, and obtaining a first corrected image sequence.
And a second step of performing image correction on the target image to generate a second corrected image.
As an example, the execution subject described above may generate the first correction image sequence and the second correction image according to fig. 3.
The specific method comprises the following steps: the coordinates of four edge points of the conveyor belt are determined in the image sequence and the target image through a drawing tool in windows, and then a quadrilateral area formed by connecting the four points a, b, c and d is projectively transformed into a rectangular area according to the moving direction of the conveyor belt, so that a first correction image sequence and a second correction image can be obtained.
And thirdly, performing noise reduction processing on each first correction image in the first correction image sequence to generate a first noise reduction image, so as to obtain a first noise reduction image sequence.
As an example, the executing entity may perform gaussian noise reduction processing on each of the first corrected images in the first corrected image sequence to generate a first noise-reduced image, resulting in a first noise-reduced image sequence.
And fourthly, carrying out noise reduction processing on the second correction image to generate a second noise reduction image.
As an example, the execution subject may perform gaussian noise reduction processing on the second correction image to generate a second noise-reduced image.
And fifthly, extracting the edge information of the first noise-reduced image sequence and the second noise-reduced image by utilizing an edge detection algorithm and/or a contour detection algorithm to obtain edge images corresponding to the edge image sequence and the target image. Wherein, the edge detection algorithm may include, but is not limited to, at least one of the following: sobel edge detection, Prewitt edge detection, Canny edge detection.
As an example, the first noise-reduced image in the first sequence of noise-reduced images may be as shown at 401 in fig. 4. The first contour detection may be as shown at 402 in fig. 4. The second contour detection may be as shown at 403 in fig. 4. The first edge detection may be as shown at 404 in fig. 4. Wherein, the first contour detection criterion may be: the binary threshold value 20 is adapted. The criteria for the second contour detection may be: a global binarization threshold 127. The first edge detection may be Canny edge detection.
The second reduced noise image may be as shown at 405 in fig. 4. The third contour detection may be as shown at 406 in fig. 4. A fourth profile detection may be shown as 407 in fig. 4. The second edge detection may be as shown at 408 in fig. 4. Wherein, the first contour detection criterion may be: the binary threshold value 20 is adapted. The criteria for the second contour detection may be: a global binarization threshold 127. The first edge detection may be Canny edge detection.
And 203, generating a statistical chart representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image.
In some embodiments, the execution subject may generate a statistical map representing the abnormal condition of the conveyor belt according to the edge image corresponding to the edge image sequence and the target image.
As an example, the execution subject may generate the statistical map by:
firstly, subtracting the pixel set corresponding to each edge image in the edge image sequence from the pixel set corresponding to the edge image corresponding to the target image to obtain a subtracted edge image sequence.
And secondly, multiplying each subtracted back edge image in the subtracted back edge image sequence by corresponding pixels to obtain a multiplied image.
And thirdly, performing evolution processing on each pixel in the multiplied image according to the number of the edge image sequences in the edge image sequences to obtain a processed image.
And fourthly, inputting the processed image into a target activation function to obtain an output result. The target activation function may be a Linear rectification function (ReLU) activation function.
And fifthly, visualizing the output result to obtain the statistical chart. As an example, the execution subject may use a related visualization tool to visualize the output result, resulting in the statistical map. The visualization tool may be a Python visualization library.
And step 204, sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
In some embodiments, the execution body may sequentially generate the abnormal information of the conveyor belt according to a predetermined sequence in the statistical chart. The abnormal information of the conveyor belt may include, but is not limited to: the conveyor belt becomes clogged with objects, the conveyor belt partially breaks, and there is a sticky matter on the conveyor belt. For example, the execution body may sequentially generate each of the abnormal information of the conveyor belt in various ways according to a predetermined order in the statistical chart.
In some optional implementation manners of some embodiments, the sequentially generating the abnormal information of the conveyor belt according to a preset sequence in the statistical chart may include:
firstly, according to the direction of the abscissa axis in the statistical chart, sequentially determining a coordinate point set of which the ordinate value is greater than a first threshold value. As an example, the above-described first threshold value may be "0.2".
A second step of, for each coordinate point in the set of coordinate points, performing the following processing steps to generate abnormality information corresponding to the coordinate point:
a first substep of determining a set of ordinate values of at least one coordinate point within a predetermined interval centered on the abscissa of said coordinate point. As an example, the predetermined section may be a section of 10 to 30 pixels to the left and right of the abscissa of the coordinate point as the center.
And a second substep of determining whether the mean value corresponding to the set of longitudinal coordinate values is greater than a second threshold value. As an example, the second threshold may be half of the first threshold.
A third substep of determining a location of congestion of the item on the conveyor belt corresponding to the coordinate point in response to determining that the mean value is greater than the second threshold. The item congestion position may be a pixel coordinate in the image.
As an example, as shown in fig. 5, for a certain image in the left image sequence in fig. 5, the statistical chart may be shifted in a clockwise direction, and the statistical chart on the right in fig. 5 may be obtained. Wherein the first threshold is 0.2. Therefore, through one-to-one comparison between the statistical chart and the image, the situation that the image is an article jam position area in the dotted line area and the article jam possibly exists in the dotted line area can be obtained.
A fourth substep of generating abnormality information of the conveyor belt based on the article jam position. As an example, the execution subject may capture a congestion situation of a congestion position of the article by the related image capturing apparatus to generate the abnormality information of the conveyor belt.
Optionally, the foregoing steps further include:
and a first step of determining an article blocking position on the conveyor belt corresponding to the coordinate point in response to the average value being less than or equal to the second threshold value.
The specific article blocking position may refer to the determination of the article blocking position, and is not described herein again.
And a second step of generating abnormal information of the transmission belt according to the article shielding position.
As an example, the execution main body may capture a blocking situation of a blocking position of the article by the related image pickup apparatus to generate the abnormality information of the conveyor belt.
The above embodiments of the present disclosure have the following beneficial effects: according to the abnormal information generation method of some embodiments of the disclosure, the abnormal condition of the conveyor belt in the process of transporting the object can be quickly and efficiently determined. Particularly, machine learning algorithm detection and recognition based on machine vision require a large amount of labeling and training, and meanwhile, the calculation complexity is high, and the generalization capability is limited. The generation of abnormal information of the conveyor belt is not efficient and rapid enough. Based on this, the anomaly information generation method of some embodiments of the present disclosure may first extract the image sequence and the target image from the target video for subsequent data support as anomaly information generation. Then, the edge information of the image sequence and the target image is extracted, and an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image are obtained. Here, the edge image sequence corresponding to the image sequence and the edge image corresponding to the target image are one of very important image features. The edge image retains the important partial image information in the original image, and the abnormal information of the conveyor belt can be effectively generated by analyzing the image information. And generating a statistical chart representing the abnormal condition of the conveyor belt according to the edge image corresponding to the edge image sequence and the target image. The abnormal information of the conveyor belt can be displayed more simply, conveniently and intuitively through the generated statistical chart. And finally, sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart. In a word, the abnormal information generation method can quickly and efficiently determine the abnormal condition of the conveyor belt in the process of transporting the object. And a large amount of labels and training are not needed, and meanwhile, the calculation complexity is low and the generalization capability is strong.
With further reference to fig. 6, a flow 600 of further embodiments of an anomaly information generation method according to the present disclosure is shown. The abnormal information generation method comprises the following steps:
In some embodiments, the specific implementation of steps 601 and 602 and the technical effects thereof can refer to steps 201 and 202 in the embodiment corresponding to fig. 2, which are not described herein again.
In some embodiments, an executing subject (e.g., the electronic device shown in fig. 1) may perform a first pooling operation on each edge image in the above-described sequence of edge images to generate a first pooled image, resulting in a first pooled image sequence. Wherein the first pooling operation may be, but is not limited to, one of: average pooling and maximum pooling.
In some embodiments, the executing entity may perform a second pooling operation on the edge image corresponding to the target image to generate a second pooled image. Wherein the second pooling operation may be, but is not limited to, one of: average pooling and maximum pooling.
Step 605 is generating the statistical map based on the first pooled image sequence and the second pooled image.
In some embodiments, the execution subject may generate the statistical map in various ways according to the first pooled image sequence and the second pooled image.
In some optional implementations of some embodiments, the generating the statistical map according to the first pooled image sequence and the second pooled image may include:
firstly, adding the pixel value sets of each line in the second pooled image to obtain a first vector.
And secondly, multiplying corresponding pixels of each first pooled image in the first pooled image sequence to obtain a multiplied image.
And thirdly, performing evolution processing on each pixel in the multiplied image according to the number of the first pooled images in the first pooled image sequence to obtain a processed image.
As an example, if the number of first pooled images in the first pooled image sequence is 3, the multiplied image is processed to the power of 3 for each pixel, so as to obtain a processed image.
And fourthly, adding the pixel value sets of each line in the processed image to obtain a second vector. The data dimension of the second vector is the same as the first vector.
And a fifth step of generating the statistical chart according to the first vector and the second vector.
As an example, the execution body may generate the statistical map by various methods according to the first vector and the second vector.
Optionally, the generating the statistical graph according to the first vector and the second vector may include:
in the first step, the first vector is subtracted from the second vector to obtain a third vector.
It should be noted that the influence of the conveyor belt characteristic information on determining whether the conveyor belt is abnormal can be removed by subtracting the first vector from the second vector. Therefore, the subsequently generated abnormal information is more accurate.
And secondly, inputting the third vector into a target activation function to obtain an output result. The target activation function may be a ReLU activation function.
And thirdly, performing moving average processing on the output result to obtain a processing result.
And fourthly, visualizing the processing result to obtain the statistical chart. As an example, the execution subject may use a related visualization tool to visualize the processing result, so as to obtain the statistical graph. The visualization tool may be a Python visualization library.
And 606, sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
In some embodiments, the specific implementation of step 606 and the technical effect thereof may refer to step 204 in the embodiment corresponding to fig. 2, which is not described herein again.
As can be seen from fig. 6, compared with the description of some embodiments corresponding to fig. 2, the flow 600 of the abnormal information generating method in some embodiments corresponding to fig. 6 highlights the specific steps of performing the pooling process on the edge image sequence and the edge image corresponding to the target image. Therefore, the solutions described in the embodiments can effectively alleviate the problem of non-smoothness between adjacent pixels caused by extracting the edge information of the image sequence and the target image by pooling the edge image sequence and the edge image corresponding to the target image, and can make the subsequently generated statistical graph and corresponding abnormal information more accurate.
With further reference to fig. 7, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of an anomaly information generating apparatus, which correspond to those illustrated in fig. 2, and which may be applied in various electronic devices in particular.
As shown in fig. 7, an abnormality information generating apparatus 700 includes: an extraction unit 701, an extraction unit 702, a first generation unit 703, and a second generation unit 704. Wherein the extraction unit 701 is configured to: and extracting an image sequence and a target image from the target video, wherein the target image is an image of the conveyor belt without the article to be transported, and the image in the image sequence is an image of the article transported by the conveyor belt. The extraction unit 702 is configured to: and extracting the edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image. The first generating unit 703 is configured to: and generating a statistical chart representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image. The second generating unit 704 is configured to: and sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
In some optional implementations of some embodiments, the first generating unit 703 in the apparatus 700 described above may be further configured to: performing a first pooling operation on each edge image in the edge image sequence to generate a first pooled image, so as to obtain a first pooled image sequence; performing a second pooling operation on the edge image corresponding to the target image to generate a second pooled image; and generating the statistical map according to the first pooling image sequence and the second pooling image.
In some optional implementations of some embodiments, the first generating unit 703 in the apparatus 700 described above may be further configured to: adding the pixel value sets of each line in the second pooled image to obtain a first vector; multiplying each first pooled image in the first pooled image sequence by corresponding pixels to obtain a multiplied image; performing evolution processing on each pixel in the multiplied image according to the number of first pooled images in the first pooled image sequence to obtain a processed image; adding the pixel value sets of each line in the processed image to obtain a second vector, wherein the data dimension of the second vector is the same as that of the first vector; and generating the statistical chart according to the first vector and the second vector.
In some optional implementations of some embodiments, the first generating unit 703 in the apparatus 700 described above may be further configured to: subtracting the first vector from the second vector to obtain a third vector; inputting the third vector to a target activation function to obtain an output result; carrying out moving average processing on the output result to obtain a processing result; and visualizing the processing result to obtain the statistical chart.
In some optional implementations of some embodiments, the second generating unit 704 in the apparatus 700 described above may be further configured to: sequentially determining a coordinate point set with a vertical coordinate value larger than a first threshold value according to the direction of the horizontal axis in the statistical chart; for each coordinate point in the set of coordinate points, performing the following processing steps to generate abnormal information corresponding to the coordinate point: determining a set of ordinate values of at least one coordinate point within a predetermined interval centered on an abscissa of the coordinate point; determining whether the mean value corresponding to the longitudinal coordinate value set is greater than a second threshold value; in response to determining that the average value is greater than the second threshold value, determining a location of an item jam on the conveyor belt corresponding to the coordinate point; and generating the abnormal information of the transmission belt according to the article jam position.
In some optional implementations of some embodiments, the second generating unit 704 in the apparatus 700 described above may be further configured to: determining an article shielding position on the conveyor belt corresponding to the coordinate point in response to the average value being less than or equal to the second threshold value; and generating the abnormal information of the transmission belt according to the article shielding position.
In some optional implementations of some embodiments, the extracting unit 701 in the apparatus 700 described above may be further configured to: carrying out image correction on each image in the image sequence to generate a first corrected image, and obtaining a first corrected image sequence; performing image correction on the target image to generate a second corrected image; performing noise reduction processing on each first correction image in the first correction image sequence to generate a first noise reduction image, so as to obtain a first noise reduction image sequence; performing noise reduction processing on the second correction image to generate a second noise reduction image; and extracting the edge information of the first noise-reduced image sequence and the second noise-reduced image by utilizing an edge detection algorithm and/or a contour detection algorithm to obtain edge images corresponding to the edge image sequence and the target image.
It will be understood that the elements described in the apparatus 700 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 700 and the units included therein, and will not be described herein again.
Referring now to fig. 8, a schematic diagram of an electronic device (e.g., the electronic device of fig. 1) 800 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM 802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 8 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through communications device 809, or installed from storage device 808, or installed from ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: extracting an image sequence and a target image from a target video, wherein the target image is an image of an article to be transported on a conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt; extracting the edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image; generating a statistical graph representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image; and sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an extraction unit, a first generation unit, and a second generation unit. The names of these units do not limit the units themselves in some cases, and for example, the second generation unit may be described as "a unit that sequentially generates each piece of abnormality information of the conveyor belt in the order set in advance in the statistical chart".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. An abnormal information generating method includes:
extracting an image sequence and a target image from a target video, wherein the target image is an image of an article to be transported on a conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt;
extracting the edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image;
generating a statistical graph representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image;
and sequentially generating each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
2. The method according to claim 1, wherein the generating a statistical map characterizing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image comprises:
performing a first pooling operation on each edge image in the edge image sequence to generate a first pooled image, so as to obtain a first pooled image sequence;
performing a second pooling operation on the edge image corresponding to the target image to generate a second pooled image;
and generating the statistical chart according to the first pooling image sequence and the second pooling image.
3. The method of claim 2, wherein the generating the statistical map from the first sequence of pooled images and the second pooled image comprises:
adding the pixel value sets of each line in the second pooled image to obtain a first vector;
multiplying corresponding pixels of each first pooled image in the first pooled image sequence to obtain a multiplied image;
performing evolution processing on each pixel in the multiplied image according to the number of first pooled images in the first pooled image sequence to obtain a processed image;
adding the pixel value sets of each line in the processed image to obtain a second vector, wherein the data dimension of the second vector is the same as that of the first vector;
and generating the statistical graph according to the first vector and the second vector.
4. The method of claim 3, wherein the generating the statistical map from the first vector and the second vector comprises:
subtracting the first vector from the second vector to obtain a third vector;
inputting the third vector to a target activation function to obtain an output result;
carrying out moving average processing on the output result to obtain a processing result;
and visualizing the processing result to obtain the statistical chart.
5. The method according to claim 1, wherein the sequentially generating the abnormal information of the conveyor belt according to the preset sequence in the statistical chart comprises:
sequentially determining coordinate point sets with ordinate values larger than a first threshold value according to the directions of the abscissa axes in the statistical chart;
for each coordinate point in the set of coordinate points, performing the following processing steps to generate abnormality information corresponding to the coordinate point:
determining a set of ordinate values for at least one coordinate point within a predetermined interval centered on an abscissa of the coordinate point;
determining whether the mean value corresponding to the longitudinal coordinate value set is greater than a second threshold value;
in response to determining that the mean value is greater than the second threshold value, determining a location of an item jam on the conveyor belt corresponding to the coordinate point;
and generating abnormal information of the transmission belt according to the article jam position.
6. The method of claim 5, wherein the method further comprises:
in response to the mean value being less than or equal to the second threshold value, determining an article blocking position on the conveyor belt corresponding to the coordinate point;
and generating abnormal information of the transmission belt according to the article shielding position.
7. The method according to claim 1, wherein the extracting edge information of the image sequence and the target image to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image comprises:
performing image correction on each image in the image sequence to generate a first corrected image, so as to obtain a first corrected image sequence;
performing image correction on the target image to generate a second corrected image;
performing noise reduction processing on each first correction image in the first correction image sequence to generate a first noise reduction image, so as to obtain a first noise reduction image sequence;
performing noise reduction processing on the second correction image to generate a second noise reduction image;
and extracting the edge information of the first noise-reduced image sequence and the second noise-reduced image by utilizing an edge detection algorithm and/or a contour detection algorithm to obtain edge images corresponding to the edge image sequence and the target image.
8. An abnormality information generation apparatus comprising:
the extraction unit is configured to extract an image sequence and a target image from a target video, wherein the target image is an image of an article to be transported which does not exist on a conveyor belt, and the image in the image sequence is an image of the article transported by the conveyor belt;
an extracting unit, configured to extract edge information of the image sequence and the target image, to obtain an edge image sequence corresponding to the image sequence and an edge image corresponding to the target image;
the first generation unit is configured to generate a statistical graph representing the abnormal condition of the conveyor belt according to the edge image sequence and the edge image corresponding to the target image;
and the second generation unit is configured to sequentially generate each abnormal information of the conveyor belt according to a preset sequence in the statistical chart.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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