CN112560779B - Method and equipment for identifying overflow of feeding port and feeding control system of stirring station - Google Patents
Method and equipment for identifying overflow of feeding port and feeding control system of stirring station Download PDFInfo
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Abstract
The embodiment of the invention provides a method and equipment for identifying overflow at a feed inlet and a feed control system of a stirring station. The method comprises the following steps: the identification method of the feed inlet flash comprises the following steps: acquiring a feeding image of the feeding hole; carrying out semantic segmentation on the feeding image to obtain a first pixel set of the feeding hole and a second pixel set of the material at the feeding hole; and judging the region coincidence relation of the first pixel set and the second pixel set, and determining the flash risk of the material at the feed inlet. According to the technical scheme, the overflow warning of the feed inlet of the stirring truck of the stirring station can be realized, the automation and intelligence level of the stirring station can be further improved, the labor cost is saved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the field of intelligent monitoring of mixing stations, in particular to a feed inlet flash identification method, a feed inlet flash identification device and a mixing station feed control system.
Background
At present, a monitoring camera is used for transmitting a monitoring image to a central control room in real time for monitoring the change of the liquid level of concrete in a feed inlet by related personnel in the central control room in real time manually through the monitoring image, if the liquid level is too high or overflows, the flow of the concrete is properly reduced by manual control, and if the liquid level is too low, the flow is increased by manual control. The manual judgment method needs all-weather duty of related personnel and consumes larger human resources.
In the prior art, an image is adopted to automatically judge, but the adopted image recognition algorithm has the defects of low robustness, weak anti-interference capability and incapability of all-weather and steady recognition.
Disclosure of Invention
The invention aims to provide a feed inlet flash identification method, equipment and a stirring station feed control system, which aim to solve the problems of low automation degree and image identification accuracy in the existing feed inlet flash monitoring and identification.
In order to achieve the above object, in a first aspect of the present invention, there is provided a method for identifying a burr of a feed port, the method comprising: acquiring a feeding image of the feeding hole; carrying out semantic segmentation on the feeding image to obtain a first pixel set of the feeding hole and a second pixel set of the material at the feeding hole; and judging the region coincidence relation of the first pixel set and the second pixel set, and determining the flash risk of the material at the feed inlet.
Preferably, the image is a single frame video frame in a monitoring video, and the monitoring video includes the feed inlet and the materials in the feed inlet.
Preferably, before the semantic segmentation is performed on the image, the identification method further includes: the image is preprocessed, wherein the preprocessing comprises image filtering and/or image homogenization.
Preferably, the first and second sets of pixels are represented as binary images.
Preferably, before determining the positional relationship between the first pixel set and the second pixel set, the identification method further includes: and carrying out post-processing on the first pixel set and the second pixel set, wherein the post-processing comprises image morphology processing and misrecognized area filtering processing.
Preferably, determining the region overlapping relationship between the first pixel set and the second pixel set, and determining the flash risk of the material at the feed inlet includes: acquiring a first edge point set of the first pixel set and a second edge point set of the second pixel set; extracting the relative distance between the first edge point set and the edge outermost tangent line of the second edge point set; and determining that the relative distance is smaller than a preset distance threshold value, and generating flash early warning.
Preferably, after generating the flash warning, the method further comprises: and adjusting the flow of the material entering the feeding port.
In a second aspect of the present invention, there is also provided a feed port burr identification apparatus, the identification apparatus comprising: at least one processor; a memory coupled to the at least one processor; the at least one processor implements the aforementioned feed inlet flash identification method by executing the instructions stored in the memory.
Preferably, the identification device is an AI chip.
In a third aspect of the present invention, there is also provided a feeding control system of a stirring station, including a monitoring camera for acquiring a feeding image of a feeding port, the feeding control system of a stirring station further including: the feed inlet flash identification device is used for identifying the flash risk of the material at the feed inlet; the feeding device is used for controlling the feeding flow based on the flash risk, and the feeding reminding device is used for generating a flash reminder based on the flash risk.
In a fourth aspect of the present invention, there is also provided a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the aforementioned method of feed port flash identification.
The technical scheme provided by the invention has the following beneficial effects:
according to the automatic judgment algorithm for the flash identification of the mixer truck based on semantic segmentation, the technical scheme can realize the flash early warning of the feed inlet of the mixer truck, further improve the automation and intelligence level of the mixer truck, save labor cost and improve work efficiency.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for identifying a feed inlet flash in an embodiment of the invention;
FIG. 2 is a schematic illustration of the zone coincidence relationship of a method of feed control to a mixing plant in accordance with one embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method of identifying a fill port flash in an embodiment of the present invention;
fig. 4 is a schematic diagram of a feed control system for a mixing plant in accordance with one embodiment of the invention.
Detailed Description
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without collision.
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Fig. 1 is a flow chart of a method for identifying a feed port flash according to an embodiment of the invention. As shown in fig. 1, in one embodiment provided by the present invention, there is provided a method for identifying a burr at a feed inlet, the method comprising:
s01, acquiring a feeding image of the feeding hole;
the image is the basis of the judgment. The image needs to comprise a feed inlet, and materials are also present in the feed inlet in the process of feeding, so that static information of the feed inlet and dynamic information of the materials can be acquired by collecting the image of the feed inlet. The image is acquired by the camera and other image acquisition devices to the feed inlet and the materials in the feed inlet, and the processing device is connected with the image acquisition device to acquire the image.
S02, carrying out semantic segmentation on the feeding image to obtain a first pixel set of the feeding port and a second pixel set of the material at the feeding port;
and carrying out semantic segmentation processing on the acquired image, identifying a feed inlet and materials from the acquired image, and representing the acquired image in a pixel mode. The present embodiment adopts the following steps: constructing two sample libraries, wherein the foreground labels of the sample libraries are respectively the concrete liquid level of a feed inlet and a feed inlet of the mixer truck; and respectively training a semantic segmentation model by using the two sample libraries, and identifying a mixer truck feed inlet area and a concrete liquid level area by using the two semantic segmentation models to finally obtain two binary images of the two. The trained semantic segmentation model can mark pixels in the image, so that different areas are extracted from the image, and targets corresponding to each area are marked. In this embodiment, the feed inlet and the material obtained by semantic segmentation are actually a collection of pixels of the feed inlet and the material in the image, respectively.
S03, judging the region coincidence relation of the first pixel set and the second pixel set, and determining the flash risk of the material at the feed inlet.
And processing the two pixel sets in the last step to obtain parameters such as the coincidence relation between the areas in the image representing the distribution of the two pixel sets, and judging the filling condition of the material inlet and the material in the image by judging the coincidence relation between the areas so as to determine whether the material in the actual scene has the flash risk exceeding the material inlet.
Because the feeding scene of the material entering the feeding hole is a natural scene, the foreground and background areas are complex and changeable, and the robustness of the traditional image processing algorithm is not strong, the feeding hole area is identified by adopting a semantic segmentation model in deep learning. According to the embodiment, the region information of the material inlet and the material in the image is identified through a semantic segmentation method, and the flash risk judgment of the material is realized through the mutual superposition relationship between the region information. The semantic segmentation is classification on the pixel level, and pixels belonging to the same class are classified, so that the semantic segmentation is to understand images from the pixel level, and has the advantage of strong image feature recognition robustness compared with other image processing modes.
In one embodiment of the present invention, the image is a single frame video frame in a monitoring video, and the monitoring video includes the material in the material inlet and the material inlet. Most of the existing field monitoring technologies adopt video monitoring, real-time video of a stirring station in the feeding process is obtained, and semantic segmentation is mostly based on images. Therefore, according to the embodiment, the video frames are obtained by extracting the single frames from the video, so that the use scene is not limited to the single image, and the continuous monitoring video stream can be processed in real time.
In one embodiment of the present invention, before performing semantic segmentation on the image, the determining method further includes: the image is preprocessed, wherein the preprocessing comprises image filtering and/or image homogenization. Good image quality is beneficial to improving the accuracy of processing. In order to reduce the influence of noise and uneven illumination on the extraction of the edge of the concrete liquid surface, pretreatment such as filtering illumination homogenization and the like is needed to be carried out on the model input image.
In one embodiment of the present invention, the first set of pixels and the second set of pixels are represented as binary images. The binary image refers to an image in which each pixel is a foreground or a background. The binary image is generally used for describing foreground and background region results after image segmentation, and the edge information is displayed in a focused manner, so that the texture feature expression in the image is ignored. The pixel set is expressed as a binary image in the embodiment, so that accurate extraction of materials and a feed inlet from the image is facilitated.
In one embodiment of the present invention, before determining the positional relationship between the first pixel set and the second pixel set, the determining method further includes: and carrying out post-processing on the first pixel set and the second pixel set, wherein the post-processing comprises image morphology processing and misrecognized area filtering processing. The post-processing mainly comprises morphological processing, including operations of corrosion, expansion, filtering of small objects and the like, and aims to filter noise and false recognition areas in binary images generated by a semantic segmentation model, so that the robustness of the whole algorithm is improved, and the post-processed images are binary images or images capable of highlighting edge effects.
In one embodiment of the present invention, determining the region overlapping relationship between the first pixel set and the second pixel set, and determining the flash risk of the material at the feed inlet includes: acquiring a first edge point set of the first pixel set and a second edge point set of the second pixel set; extracting the relative distance between the first edge point set and the edge outermost tangent line of the second edge point set; and determining that the relative distance is smaller than a preset distance threshold value, and generating flash early warning. And extracting the relative distance d of the tangential lines at the outermost ends of the edges of the feeding port and the concrete liquid surface according to the edge point set of the feeding port and the concrete liquid surface. Fig. 2 is a schematic diagram of a region overlapping relationship of a feeding control method of a mixing station according to an embodiment of the present invention, as shown in fig. 2, where d is a distance between bottom edges of minimum external moments of two edge point sets. And setting the threshold value as threshold, if d is less than or equal to threshold, carrying out flash early warning, otherwise, determining that the flash condition is normal.
In one embodiment of the present invention, after generating the flash warning, the method further includes: and adjusting the flow of the material entering the feeding port. And when the judgment result in the previous step is that the overflow is early-warning, the flow of the material entering the feed inlet is required to be regulated, and the accumulated amount of the material at the feed inlet is reduced by reducing the feed flow. For example, when the risk of material overflow at the feed inlet is detected to be low, the feed flow rate can be increased, so that the feed efficiency is improved, and the feed time is shortened. Or when the flash risk is high, the feed flow rate can be reduced to avoid flash. The above embodiments facilitate providing a technical basis for automatic feed control.
Fig. 3 is a flowchart illustrating an implementation of the method for identifying a feed port flash in an embodiment of the present invention. When an obtained single-frame video frame containing a feed inlet and materials in the feed inlet is subjected to preprocessing and then is input into a semantic segmentation model for semantic segmentation, a binary segmentation image of the materials (such as concrete) of the feed inlet or the feed inlet is obtained, an edge point set of the liquid level of the concrete is calculated after the post-processing, the edge point set of the liquid level of the concrete is compared with the edge point set of the feed inlet of a mixer truck, the relative distance between the tangential lines of the outermost ends of the two edges is obtained, and whether flash and the risk probability of the flash are judged based on the relative distance.
In one embodiment of the present invention, there is also provided a feed port burr identification apparatus, the identification apparatus including: at least one processor; a memory coupled to the at least one processor; the at least one processor implements the aforementioned feed inlet flash identification method by executing the instructions stored in the memory. The processor has the functions of numerical calculation and logic operation, and at least has a central processing unit CPU, a random access memory RAM, a read only memory ROM, various I/O ports, an interrupt system and the like with data processing capability. The data processing module may be, for example, a common hardware such as a single chip microcomputer, a chip or a processor, and in a more common case, a processor of an intelligent terminal or a PC. Here, the apparatus may be a control computer in the concrete mixing plant or an existing controller in the automatic discharge system, the function it performs being a sub-function of the control computer or controller. In the form of a piece of software code in a hardware operating environment that relies on an existing control computer or controller.
In one embodiment of the present invention, the identification device is an AI chip. AI chips are modules dedicated to handling a large number of computing tasks in artificial intelligence applications, with hardware architecture adapted to AI computing, and therefore processing speed is faster and energy efficient. According to the embodiment, the monitoring image is analyzed and judged in real time through the artificial intelligence algorithm in the AI chip, so that the intelligent monitoring image processing method not only can realize the intellectualization, but also has the advantages of rapid processing and high accuracy.
Fig. 4 is a schematic diagram of a feed control system of a mixing plant in accordance with an embodiment of the invention, as shown in fig. 4. In this embodiment, still provide a stirring station feed control system, including the surveillance camera head, the surveillance camera head is used for gathering the feed image of feed inlet, stirring station feed control system still includes: the feeding flow sensor is used for acquiring the feeding flow of the material at the feeding port; the feed inlet flash identification device is used for identifying the flash risk of the material at the feed inlet; the feeding device is used for controlling the feeding flow based on the flash risk, and the feeding reminding device is used for generating a flash reminder based on the flash risk. In this application scenario. When the stirring truck of the stirring station is in a feeding process and needs feeding control, a monitoring camera is used for acquiring an original monitoring scene to obtain an image, and an AI chip is optimized in the embodiment through the feeding port flash identification equipment, and the AI chip is stored with a deep learning model. After the image is processed by the AI chip, a flash judgment result is output, and the flash judgment result can be displayed by adopting a vehicle-mounted display screen or a feeding reminding device such as voice reminding information or lamplight reminding information; the flash judgment result can also be input to the feeding control device, so that the feeding control device controls the feeding flow of the material. The control of the feed flow in such control methods may be qualitative, including only maintenance, increase and decrease. The feed flow sensor in this embodiment is used to provide quantitative feed flow data and may provide data support for quantitative adjustment of flow. The feeding control system of the stirring station provided in the embodiment has the advantage of high automation degree, and lays a technical foundation for realizing automatic feeding of a stirring vehicle.
In one embodiment of the present invention, a computer readable storage medium is provided, in which instructions are stored, which when run on a computer, cause the computer to perform the aforementioned method of feed port flash identification. The operation entity of the method is a PC or an intelligent terminal or a server.
The embodiment of the invention provides a method and equipment for identifying overflow of a feed inlet, and the method mainly comprises image acquisition, semantic segmentation, overflow judgment and the like, and simultaneously provides a feed control system of a mixing station, which can automatically control the feed flow in the feed process, improve the feed efficiency and effectively improve the automation degree of loading. The invention can reduce the flash risk of the feeding in the stirring station and has wide application scenes.
The foregoing details of the optional implementation of the embodiment of the present invention have been described in detail with reference to the accompanying drawings, but the embodiment of the present invention is not limited to the specific details of the foregoing implementation, and various simple modifications may be made to the technical solution of the embodiment of the present invention within the scope of the technical concept of the embodiment of the present invention, and these simple modifications all fall within the protection scope of the embodiment of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, various possible combinations of embodiments of the present invention are not described in detail.
Those skilled in the art will appreciate that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, including instructions for causing a single-chip microcomputer, chip or processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In addition, any combination of various embodiments of the present invention may be performed, so long as the concept of the embodiments of the present invention is not violated, and the disclosure of the embodiments of the present invention should also be considered.
Claims (9)
1. A method for identifying a feed port flash, the method comprising:
acquiring a feeding image of the feeding hole;
carrying out semantic segmentation on the feeding image to obtain a first pixel set of the feeding hole and a second pixel set of the material at the feeding hole;
judging the region coincidence relation of the first pixel set and the second pixel set, and determining the flash risk of the material at the feed inlet, wherein the method comprises the following steps:
acquiring a first edge point set of the first pixel set and a second edge point set of the second pixel set; extracting the relative distance between the first edge point set and the edge outermost tangent line of the second edge point set; and determining that the relative distance is smaller than a preset distance threshold value, and generating flash risk early warning.
2. The method of claim 1, wherein the image is a single frame of video in a surveillance video, the surveillance video including the feed port and the material in the feed port.
3. The method of claim 1, wherein prior to semantically segmenting the image, the method further comprises: the image is preprocessed, wherein the preprocessing comprises image filtering and/or image homogenization.
4. The method of claim 1, wherein the first and second sets of pixels are represented as binary images.
5. The identification method according to claim 1 or 4, characterized in that before judging the region coincidence relation of the first pixel set and the second pixel set, the identification method further comprises: and carrying out post-processing on the first pixel set and the second pixel set, wherein the post-processing comprises image morphology processing and misrecognized area filtering processing.
6. The identification method of claim 1, wherein after generating the flash risk warning, the method further comprises: and adjusting the flow of the material entering the feeding port.
7. A feed port flash identification apparatus, the identification apparatus comprising:
at least one processor;
a memory coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the at least one processor implementing the feed port flash identification method of any one of claims 1 to 6 by executing the instructions stored by the memory.
8. The identification device of claim 7, wherein the identification device is an AI chip.
9. The utility model provides a stirring station feed control system, includes the surveillance camera head, the surveillance camera head is used for gathering the feed image of feed inlet, its characterized in that, stirring station feed control system still includes:
a feed port flash identification device as claimed in claim 7 or 8 for identifying the risk of flash of material at the feed port;
the feeding device is used for controlling feeding flow based on the flash risk, and the feeding reminding device is used for generating flash reminding based on the flash risk.
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| CN113554001B (en) * | 2021-09-18 | 2022-04-01 | 三一汽车制造有限公司 | Mixer truck flash identification method and system, electronic equipment and mixing station |
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| CN112016533A (en) * | 2020-10-26 | 2020-12-01 | 天津美腾科技股份有限公司 | Material detection method and device, electronic equipment and computer readable storage medium |
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