CN115797279A - Method and device for analyzing abnormity of capital construction site, electronic equipment and storage medium - Google Patents
Method and device for analyzing abnormity of capital construction site, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses an anomaly analysis method and device for a capital construction site, electronic equipment and a storage medium. Belongs to the technical field of image analysis and is used for safety control of capital construction sites. The method comprises the following steps: acquiring a video image of a target object of a construction site, wherein the target object comprises one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures; inputting the video image into a target segmentation algorithm model corresponding to the target object to obtain a target segmentation area of the target object; and performing anomaly analysis on the target object based on the target segmentation region of the target object.
Description
Technical Field
The application belongs to the technical field of image analysis, and particularly relates to an anomaly analysis method and device for a capital construction site, electronic equipment and a storage medium.
Background
At present, the national economy and the production and life of people are influenced by infrastructure construction in multiple fields, a tower crane is often used as a building material for lifting reinforcing steel bars and the like on a capital construction site, and if the building material lifted by the tower crane is unbalanced, the building material falls from the air, and great safety threat is generated to operating personnel under the tower crane; the steel wire rope in the tower crane is broken, so that building materials can fall off, and great personnel life and property loss is caused; the corrosion of water stain and the pollution of dye on the concrete structure affect the service life of the constructed infrastructure if the concrete structure is not treated in time. The traditional method is to monitor and control the unbalance of hoisted objects, broken steel wire ropes and concrete structure pollution on a capital construction site by using a security inspector, and the traditional method has the disadvantages of high difficulty in safety control, low efficiency and difficulty in realizing fine management.
For this reason, real-time safety control is required at the infrastructure site.
Disclosure of Invention
The embodiment of the application provides an anomaly analysis method and device for a capital construction site, electronic equipment and a storage medium, and can solve the problem that the capital construction site needs real-time safety control.
In a first aspect, an embodiment of the present application provides an anomaly analysis method for a capital construction site, including: acquiring a video image of a target object of a construction site, wherein the target object comprises one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures; inputting the video image into a target segmentation algorithm model corresponding to the target object to obtain a target segmentation area of the target object; and performing anomaly analysis on the target object based on the target segmentation region of the target object.
In a second aspect, an embodiment of the present application provides an apparatus for analyzing an anomaly in a capital construction site, including: an acquisition module configured to acquire a video image of a target object of a construction site, wherein the target object includes one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures; the segmentation module is used for obtaining a target segmentation area of the target object by inputting the video image to a target segmentation algorithm model corresponding to the target object; and the analysis module is used for carrying out abnormity analysis on the target object based on the target segmentation region of the target object.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or an instruction stored on the memory and executable on the processor, and when the program or the instruction is executed by the processor, the method for analyzing an abnormality of a infrastructure site according to the first aspect is implemented.
In a fourth aspect, the present application provides a readable storage medium, on which a program or instructions are stored, and when executed by a processor, the program or instructions implement the steps of the method for analyzing the abnormality of the infrastructure site according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to run a program or instructions to implement the method for analyzing an anomaly in a infrastructure site according to the first aspect.
In an embodiment of the present application, a video image of a target object of a construction site is obtained, where the target object includes one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures; inputting the video image into a target segmentation algorithm model corresponding to the target object to obtain a target segmentation area of the target object; and performing anomaly analysis on the target object based on the target segmentation region of the target object. According to the technical scheme provided by the embodiment of the application, the video image of the target image is input into the target segmentation algorithm model corresponding to the target object to obtain the target segmentation area of the target object, and the target object is subjected to abnormal analysis based on the target segmentation area, wherein the target object at least comprises one of a hanging object, a steel wire rope, a concrete structure and stains on the concrete structure, so that the effect of real-time control of a construction site is achieved.
Drawings
Fig. 1 is a schematic flowchart of an anomaly analysis method in a infrastructure site according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating the training of a target segmentation algorithm model according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating the testing of a target segmentation algorithm model according to an embodiment of the present application;
fig. 4 is a schematic flow chart of an abnormality analysis apparatus in a capital construction site according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced in sequences other than those illustrated or described herein, and that the terms "first," "second," and the like are generally used herein in a generic sense and do not limit the number of terms, e.g., the first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.
The method, the apparatus, the electronic device, and the storage medium for analyzing the anomaly of the infrastructure site provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates an anomaly analysis method for a construction site according to an embodiment of the present invention, which may be performed by an electronic device, where the electronic device may include: and the terminal equipment can be a computer terminal or a mobile phone terminal and the like. In other words, the method may be performed by software or hardware installed in the terminal device, the method including the steps of:
step 101: acquiring a video image of a target object of a construction site, wherein the target object comprises one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures.
In an embodiment, a video image of at least one target object of a hanging object, a steel wire rope, a concrete structure and stains on the concrete structure on a capital construction site can be monitored and acquired through a camera installed on the capital construction site, so that the real-time performance of the video image of the target object on the capital construction site is ensured, and of course, in the embodiment of the present application, the type and the number of the cameras are not specifically limited.
Step 102: and inputting the video image to a target segmentation algorithm model corresponding to the target object to obtain a target segmentation area of the target object.
In an embodiment, the video image is input into the target segmentation algorithm model corresponding to the target object to obtain the target segmentation area of the target object, for example, the video image of the suspended object is input into the segmentation algorithm model corresponding to the suspended object to obtain the segmentation area of the suspended object, the video image of the steel wire rope is input into the segmentation algorithm model corresponding to the steel wire rope to obtain the segmentation area of the steel wire rope, the video image of the concrete structure and stains thereon is input into the segmentation algorithm model corresponding to the concrete structure to obtain the segmentation area of the concrete structure and stains thereon, and the target segmentation algorithm model is used for segmenting the target areas of different target objects, so that the accuracy of real-time control of a construction site is improved, and the effect of fine management is achieved.
Of course, in the embodiment of the present application, the target object to be monitored is not specifically limited, and may be selected according to actual requirements.
Step 103: and performing anomaly analysis on the target object based on the target segmentation region of the target object.
In one embodiment, according to the target segmentation area of the target object, abnormality analysis is performed on the target object, for example, abnormality analysis is performed on a hanging object through the segmentation area of the hanging object, abnormality analysis is performed on a steel wire rope through the segmentation area of the steel wire rope, and abnormality analysis is performed on a concrete structure and dirt on the concrete structure through the segmentation area of the concrete structure and dirt on the concrete structure, so that real-time management efficiency of a construction site is improved.
Of course, in the embodiment of the present application, the target segmentation area of the target object is not specifically limited, and may be selected according to actual requirements.
In one embodiment, as shown in fig. 2, before step 102, the following steps are further included:
step 201: a plurality of sample images of the target object are acquired.
For example, a certain number of cameras can be installed on a capital construction site to monitor hoisted objects, steel wire ropes, concrete structures and stains thereon, video materials exceeding a certain time period are collected for each scene, the video materials can be used for algorithm training, and of course, the number of cameras, the monitoring scene and the collection time are not specifically limited and can be selected according to requirements.
Step 202: and segmenting the area of the target object from the sample image to obtain a positive sample image and a negative sample image.
For example, the area of the hanging object may be divided from the sample image of the hanging object, the divided area of the hanging object is a positive sample image, the sample image of the hanging object is a negative sample image, the area of the wire rope is divided from the sample image of the wire rope, the divided area of the wire rope is a positive sample image, the sample image of the wire rope is a negative sample image, the concrete structure and the area of the dirt thereon are divided from the sample image of the concrete structure and the dirt thereon, the divided area of the concrete structure and the dirt thereon is a positive sample image, and the sample image of the concrete structure and the dirt thereon is a negative sample image.
Step 203: and respectively inputting the positive sample image and the negative sample image of the plurality of sample images into the target segmentation algorithm model to train the target segmentation algorithm model.
For example, the positive sample images and the negative sample images of the hoists are respectively input into the segmentation algorithm models of the hoists to train the segmentation algorithm models of the hoists, the positive sample images and the negative sample images of the steel wire ropes are respectively input into the segmentation algorithm models of the steel wire ropes to train the segmentation algorithm models of the steel wire ropes, the positive sample images and the negative sample images of the concrete structures and stains on the concrete structures are respectively input into the segmentation algorithm models of the concrete structures and stains on the concrete structures to train the segmentation algorithm models of the concrete structures and stains on the concrete structures, and the training of the target segmentation algorithm models can improve the accuracy of the target segmentation algorithm models in practical application.
In one embodiment, as shown in fig. 3, after step 203, the following steps are further included:
step 301: a plurality of test images of the target object are acquired.
For example, scene graphs of a plurality of hoists, steel wire ropes, concrete structures and stains on the concrete structures monitored by the camera can be used as a plurality of test images, so that the segmentation model can be conveniently trained.
Step 302: and segmenting the area of the target object from the test image to obtain a first test image and a second test image.
For example, the area of the hanging object may be divided from the test image of the hanging object as a first test image, the test image of the hanging object as a second test image, the area of the steel wire rope may be divided from the test image of the steel wire rope as a first test image, the test image of the steel wire rope as a second test image, the area of the concrete structure may be divided from the test image of the concrete structure and the dirt thereon as a first test image, and the test image of the concrete structure and the dirt thereon as a second test image.
Step 303: and respectively inputting the first test image and the second test image of the plurality of test images into the target segmentation algorithm model to obtain a plurality of test segmentation areas input by the target segmentation algorithm model.
For example, a first test image and a second test image of the hanging object can be respectively input into a segmentation model of the hanging object, the segmentation model of the hanging object is input into a plurality of test segmentation areas, the first test image and the second test image of the steel wire rope are respectively input into the segmentation model of the steel wire rope, the segmentation model of the steel wire rope is input into a plurality of test segmentation areas, the first test image and the second test image of the concrete structure and the stain on the concrete structure are respectively input into the segmentation model of the concrete structure and the stain on the concrete structure, the segmentation model of the concrete structure and the stain on the concrete structure is input into a plurality of test segmentation areas, and the input of the plurality of test segmentation areas improves the accuracy of model test.
Step 304: and determining that the accuracy of the plurality of test segmentation areas is greater than a preset value.
For example, the accuracy of the test of the hanging object segmentation model according to the plurality of test segmentation areas of the hanging object reaches 90%, the accuracy of the test of the steel wire rope segmentation model according to the plurality of test segmentation areas of the steel wire rope reaches 92%, and the accuracy of the test of the concrete structure and the stain thereon according to the plurality of test segmentation areas of the concrete structure and the stain thereon reaches 95%.
In an embodiment, in the case that the target object is a hanging object, step 103 includes:
acquiring a predicted hanging object center line vector based on the leftmost coordinate and the rightmost coordinate of the target segmentation area; acquiring an included angle between the predicted central line vector of the hanging object and a preset datum line vector; and determining that the hanging object is abnormal under the condition that the included angle is larger than a first threshold value.
For example, the leftmost seat of the divided region of the suspended object may be marked with (x) left ,y left ) The rightmost sitting mark is (x) right ,y right ) Obtaining the predicted central line vector of the hanging object and recording the vector as v pre ＝(x right -x left ,y right -y left ) The preset reference line vector is denoted as v base ＝(x base ,y base ) The included angle between the central line vector of the hanging object and the preset reference line vector is recorded as
And determining that the hanging object is abnormal under the condition that an included angle between the central line vector of the hanging object and a preset datum line vector is larger than a first threshold value.
Of course, in the embodiment of the present application, the first threshold is not particularly limited, and may be selected according to requirements.
In an embodiment, in the case that the target object is a steel wire rope, step 103 further includes:
binarizing the video image, wherein pixels of the target segmentation area of the binarized video image are first values, and pixels of the rest areas are second values, wherein the first values are one of 0 and 1, and the second values are the other of 0 and 1; performing line scanning from top to bottom on the binarized video image to obtain a leftmost first edge pixel coordinate and a rightmost second edge pixel coordinate of the target segmentation area; and determining that the steel wire rope is abnormal under the condition that the difference value of the second edge pixel coordinate and the first edge pixel coordinate is smaller than a second threshold value.
For example, the wire rope video image may be binarized, pixels of the target segmentation area of the binarized video image are set to be a first value and are set to be 1, pixels of the rest areas are set to be a second value and are set to be 0, the binarized video image is scanned from top to bottom, and the first edge pixel seating mark on the leftmost side of the target segmentation area is taken as the coordinate mark of the first edge pixel of the leftmost side of the target segmentation area
Second edge pixel coordinate marking on the rightmost edge
The difference between the second edge pixel coordinate and the first edge pixel coordinate is recorded as
And determining that the steel wire rope is abnormal under the condition that the difference value of the second edge pixel coordinate and the first edge pixel coordinate is smaller than a second threshold value.
Of course, in the embodiment of the present application, the second threshold is not particularly limited, and may be selected according to requirements.
In an embodiment, in a case that the target object is a concrete structure and dirt thereon, the target segmentation region includes a concrete structure segmentation region and a dirt segmentation region, and step 103 further includes:
determining stains on the concrete structure according to the coordinates of the stain segmentation areas and the coordinates of the concrete structure segmentation areas; acquiring a first area of the stain segmentation area and a second area of the concrete structure segmentation area; and determining that the concrete structure is abnormal under the condition that the ratio of the first area to the second area is larger than a third threshold value.
For example, the seating of the stain-dividing region may be marked
The seat marks of the concrete structure partition areas can be used as
Wherein:
respectively representing the minimum abscissa and the ordinate of a concrete structure polluted area;
respectively representing the maximum abscissa and the ordinate of a concrete structure polluted area;
respectively representing the minimum abscissa and the ordinate of the concrete structure area;
respectively representing the maximum abscissa and the ordinate of the concrete structure area; recording the first area of the obtained soil segmentation area as
Recording the second area of the concrete structure partition area as
And determining that the concrete structure is abnormal under the condition that the ratio of the first area to the second area is larger than a third threshold value.
Of course, in the embodiment of the present application, the third threshold is not particularly limited, and may be selected according to requirements.
In an embodiment, after step 103, further comprising:
and alarming under the condition that the target object is abnormal based on the average analysis of the continuously acquired video images of the n frames of the target object.
For example, for a video image of a hanging object, if the three continuous frames of video images are abnormal, an alarm is given, for a video image of a steel wire rope, if the three continuous frames of video images are abnormal in breaking of the steel wire rope, an alarm is given, for a video image of a concrete structure and stains on the concrete structure, if the three continuous frames of video images are abnormal in the concrete structure and the stains on the concrete structure, an alarm is given, and the alarm can inform a work manager in real time, so that the efficiency of safety control of a construction site is improved.
Of course, in the embodiment of the present application, the number of the video images with the abnormality of the target object is not specifically limited, and may be selected according to the requirement.
In the method for analyzing the abnormality of the infrastructure site provided in the embodiment of the present application, the execution main body may be an abnormality analysis device of the infrastructure site or a control module in the abnormality analysis device of the infrastructure site, which is used for executing the abnormality analysis method of the infrastructure site. In the embodiment of the present application, an abnormality analysis method performed by an abnormality analysis device in a capital construction site in an industrial capital construction site is taken as an example, and the abnormality analysis device in the capital construction site provided in the embodiment of the present application is described.
Fig. 4 is a schematic structural diagram of an abnormality analysis apparatus in a capital construction site according to an embodiment of the present invention. As shown in fig. 4, the abnormality analysis device in the capital construction site includes: an acquisition module 401, a segmentation module 402, and an analysis module 403; the obtaining module 401 is configured to obtain a video image of a target object of a infrastructure site, where the target object includes one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures; a segmentation module 402, configured to obtain a target segmentation region of the target object by inputting the video image to a target segmentation algorithm model corresponding to the target object; an analysis module 403, configured to perform anomaly analysis on the target object based on the target segmentation area of the target object.
In one embodiment, the training module is to: acquiring a plurality of sample images of the target object; segmenting the area of the target object from the sample image to obtain a positive sample image and a negative sample image; respectively inputting the positive sample image and the negative sample image of the plurality of sample images into the target segmentation algorithm model to train the target segmentation algorithm model.
In one embodiment, the test module is to: acquiring a plurality of test images of the target object; segmenting the region of the target object from the test image to obtain a first test image and a second test image; respectively inputting the first test image and the second test image of the plurality of test images into the target segmentation algorithm model to obtain a plurality of test segmentation areas input by the target segmentation algorithm model; and determining that the accuracy of the plurality of test division areas is greater than a preset value.
In an embodiment, the analysis module 403 performs an anomaly analysis on the target object based on the target segmentation area of the target object, including: under the condition that the target object is a hanging object, acquiring a predicted hanging object center line vector based on the leftmost coordinate and the rightmost coordinate of the target segmentation area; acquiring an included angle between the predicted central line vector of the hanging object and a preset datum line vector; and determining that the hanging object is abnormal when the included angle is larger than a first threshold value.
In an embodiment, the analysis module 403 performs an anomaly analysis on the target object based on the target segmentation region of the target object, including: under the condition that the target object is a steel wire rope, binarizing the video image, wherein pixels of the target segmentation area of the binarized video image are first values, and pixels of the rest areas are second values, wherein the first values are one of 0 and 1, and the second values are the other of 0 and 1; performing line scanning from top to bottom on the binarized video image to obtain a leftmost first edge pixel coordinate and a rightmost second edge pixel coordinate of the target segmentation area; and determining that the steel wire rope is abnormal under the condition that the difference value of the second edge pixel coordinate and the first edge pixel coordinate is smaller than a second threshold value.
In an embodiment, the analysis module 403 performs an anomaly analysis on the target object based on the target segmentation region of the target object, including: determining that the target object is abnormal based on the target segmentation area of the target object under the condition that the target object is a concrete structure and stains on the concrete structure, and further comprising: determining stains on the concrete structure according to the coordinates of the stain segmentation areas and the coordinates of the concrete structure segmentation areas; acquiring a first area of the stain segmentation area and a second area of the concrete structure segmentation area; and determining that the concrete structure is abnormal under the condition that the ratio of the first area to the second area is larger than a third threshold value.
In an embodiment, the analysis module 403 is further configured to: and alarming under the condition that the target object is abnormal based on the average analysis of the continuously acquired video images of the n frames of the target object.
The anomaly analysis device in the infrastructure site in the embodiment of the present application may be a device, or may be a component, an integrated circuit, or a chip in a terminal. The device can be mobile electronic equipment or non-mobile electronic equipment. By way of example, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine or a self-service machine, and the like, and the embodiments of the present application are not particularly limited.
The abnormality analysis device in the infrastructure site in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an ios operating system, or other possible operating systems, which is not specifically limited in the embodiment of the present application.
The anomaly analysis device for the infrastructure site provided in the embodiment of the present application can implement each process implemented in the method embodiments of fig. 1 to 3, and is not described here again in order to avoid repetition.
Based on the same technical concept, an embodiment of the present application further provides an electronic device, where the electronic device is configured to execute the above-mentioned abnormality analysis method for the infrastructure site, and fig. 5 is a schematic structural diagram of an electronic device implementing various embodiments of the present application. Electronic devices may have a relatively large difference due to different configurations or performances, and may include a processor (processor) 501, a communication Interface (Communications Interface) 502, a memory (memory) 503, and a communication bus 504, where the processor 501, the communication Interface 502, and the memory 503 complete communication with each other through the communication bus 504. The processor 501 may call a computer program that is stored in the memory 503 and is executable on the processor 501, and the specific execution steps may refer to each step of the above-described embodiment of the method for analyzing an anomaly in a capital construction site, and may achieve the same technical effect, and for avoiding repetition, details are not described here again.
It should be noted that the electronic device in the embodiment of the present application includes: a server, a terminal, or other device besides a terminal.
The above electronic device structure does not constitute a limitation of the electronic device, the electronic device may include more or less components than those shown in the drawings, or some components may be combined, or different component arrangements, for example, the input Unit may include a Graphics Processing Unit (GPU) and a microphone, and the display Unit may configure the display panel in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit includes at least one of a touch panel and other input devices. The touch panel is also referred to as a touch screen. Other input devices may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein.
The memory may be used to store software programs as well as various data. The memory may mainly include a first storage area storing a program or an instruction and a second storage area storing data, wherein the first storage area may store an operating system, an application program or an instruction (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory may include volatile memory or nonvolatile memory, or the memory may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-Only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. The volatile Memory may be a Random Access Memory (RAM), a Static Random Access Memory (Static RAM, SRAM), a Dynamic Random Access Memory (Dynamic RAM, DRAM), a Synchronous Dynamic Random Access Memory (Synchronous DRAM, SDR AM), a Double Data Rate Synchronous Dynamic Random Access Memory (Double Data Rate SD RAM, ddr SDRAM), an Enhanced Synchronous SDRAM (ESDRAM), a Synchronous Link DRAM (SLDRAM), and a Direct Memory bus RAM (DRRAM).
A processor may include one or more processing units; optionally, the processor integrates an application processor, which mainly handles operations related to the operating system, user interface, application programs, etc., and a modem processor, which mainly handles wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the above-mentioned embodiment of the method for analyzing an anomaly in a infrastructure site, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and so on.
The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction, so as to implement each process of the above embodiment of the method for analyzing an anomaly in a infrastructure site, and achieve the same technical effect, and in order to avoid repetition, the details are not repeated here.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as system-on-chip, system-on-chip or system-on-chip, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one of 8230, and" comprising 8230does not exclude the presence of additional like elements in a process, method, article, or apparatus comprising the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the present embodiments are not limited to those precise embodiments, which are intended to be illustrative rather than restrictive, and that various changes and modifications may be effected therein by one skilled in the art without departing from the scope of the appended claims.
Claims (10)
1. An anomaly analysis method for a capital construction site is characterized by comprising the following steps:
acquiring a video image of a target object of a construction site, wherein the target object comprises one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures;
inputting the video image into a target segmentation algorithm model corresponding to the target object to obtain a target segmentation area of the target object;
and performing anomaly analysis on the target object based on the target segmentation region of the target object.
2. The method according to claim 1, wherein before the obtaining of the target segmentation region of the target object through the input of the video image to the target segmentation algorithm model corresponding to the target object, the method further comprises:
acquiring a plurality of sample images of the target object;
segmenting the area of the target object from the sample image to obtain a positive sample image and a negative sample image;
and respectively inputting the positive sample image and the negative sample image of the plurality of sample images into the target segmentation algorithm model to train the target segmentation algorithm model.
3. The method of claim 2, wherein after the target segmentation algorithm model is trained by inputting the positive sample image and the negative sample image to the target segmentation algorithm model, respectively, the method further comprises:
acquiring a plurality of test images of the target object;
segmenting the region of the target object from the test image to obtain a first test image and a second test image;
respectively inputting the first test image and the second test image of the plurality of test images into the target segmentation algorithm model to obtain a plurality of test segmentation areas input by the target segmentation algorithm model;
and determining that the accuracy of the plurality of test division areas is greater than a preset value.
4. The method according to any one of claims 1 to 3, wherein, when the target object is a suspended object, performing anomaly analysis on the target object based on the target segmented region of the target object comprises:
acquiring predicted central line vectors of the hanging objects based on the leftmost coordinates and the rightmost coordinates of the target segmentation areas;
acquiring an included angle between the predicted central line vector of the hanging object and a preset datum line vector;
and determining that the hanging object is abnormal under the condition that the included angle is larger than a first threshold value.
5. The method according to any one of claims 1 to 3, wherein, in a case where the target object is a wire rope, the determining that the target object is abnormal based on the target segmented region of the target object further comprises:
binarizing the video image, wherein pixels of the target segmentation area of the binarized video image are first values, and pixels of the rest areas are second values, wherein the first values are one of 0 and 1, and the second values are the other of 0 and 1;
performing line scanning from top to bottom on the binarized video image to obtain a leftmost first edge pixel coordinate and a rightmost second edge pixel coordinate of the target segmentation area;
and determining that the steel wire rope is abnormal under the condition that the difference value of the second edge pixel coordinate and the first edge pixel coordinate is smaller than a second threshold value.
6. The method according to any one of claims 1 to 3, wherein in the case where the target object is a concrete structure and stains thereon, the target segmentation region includes a concrete structure segmentation region and a stain segmentation region;
the determining that the target object is abnormal based on the target segmentation region of the target object further includes:
determining stains on the concrete structure according to the coordinates of the stain segmentation areas and the coordinates of the concrete structure segmentation areas;
acquiring a first area of the stain segmentation area and a second area of the concrete structure segmentation area;
and under the condition that the ratio of the first area to the second area is larger than a third threshold value, determining that the concrete structure is abnormal.
7. The method according to any one of claims 1 to 3, wherein after performing anomaly analysis on the target object based on the target segmented region of the target object, the method further comprises:
and alarming when the target object is abnormal based on the video images of the n frames of the continuously acquired target object which are equally separated.
8. An abnormality analysis device for a capital construction site, comprising:
an acquisition module configured to acquire a video image of a target object of a construction site, wherein the target object includes one of: hanging objects, steel wire ropes, concrete structures and stains on the concrete structures;
the segmentation module is used for obtaining a target segmentation area of the target object by inputting the video image to a target segmentation algorithm model corresponding to the target object;
and the analysis module is used for carrying out abnormity analysis on the target object based on the target segmentation region of the target object.
9. An electronic device comprising a processor, a memory and a program or instructions stored on the memory and executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method of anomaly analysis in a construction site according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a program or instructions are stored, which program or instructions, when executed by a processor, carry out the steps of the method of anomaly analysis in a construction site according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117788458A (en) * | 2024-02-23 | 2024-03-29 | 中建安装集团有限公司 | LNG air temperature gasifier foundation concrete life analysis method, medium and system |
| CN117788458B (en) * | 2024-02-23 | 2024-05-14 | 中建安装集团有限公司 | LNG air temperature gasifier foundation concrete life analysis method, medium and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117788458A (en) * | 2024-02-23 | 2024-03-29 | 中建安装集团有限公司 | LNG air temperature gasifier foundation concrete life analysis method, medium and system |
| CN117788458B (en) * | 2024-02-23 | 2024-05-14 | 中建安装集团有限公司 | LNG air temperature gasifier foundation concrete life analysis method, medium and system |
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