CN116425047A - Crane operation alarming method, device, equipment and computer readable storage medium - Google Patents
Crane operation alarming method, device, equipment and computer readable storage medium Download PDFInfo
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- CN116425047A CN116425047A CN202211606875.7A CN202211606875A CN116425047A CN 116425047 A CN116425047 A CN 116425047A CN 202211606875 A CN202211606875 A CN 202211606875A CN 116425047 A CN116425047 A CN 116425047A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000001514 detection method Methods 0.000 claims description 23
- 239000000725 suspension Substances 0.000 claims description 15
- 238000013135 deep learning Methods 0.000 claims description 11
- 238000002372 labelling Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 8
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
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- 230000000694 effects Effects 0.000 description 1
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
- B66C15/06—Arrangements or use of warning devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C23/00—Cranes comprising essentially a beam, boom, or triangular structure acting as a cantilever and mounted for translatory of swinging movements in vertical or horizontal planes or a combination of such movements, e.g. jib-cranes, derricks, tower cranes
- B66C23/88—Safety gear
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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- Mechanical Engineering (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Helmets And Other Head Coverings (AREA)
Abstract
The invention provides a crane operation alarming method, a device, equipment and a computer readable storage medium, which are based on a plurality of cameras of a camera group, wherein the plurality of cameras can cover a crane operation area in 360 degrees, when a crane is detected to be in operation in a picture of any camera, the picture of all the cameras is used for identifying whether people targets without safety caps are arranged in the picture, so that the problem of missing identification of a single camera is avoided, for example, when people in the picture of a certain camera are blocked by the crane, the picture of other cameras is used for identifying whether people targets without safety caps are arranged in the picture of other cameras, the accuracy is high, the safety of crane operation is improved, and the mode is low in cost and convenient to deploy and use.
Description
Technical Field
The invention belongs to the technical field of crane operation alarming, and particularly relates to a crane operation alarming method, device and equipment and a computer readable storage medium.
Background
When a crane works in a warehouse or outdoor, objects are often relatively large and heavy, so that workers are required to wear safety helmets in a crane working area.
Currently, in order to find and correct the situation that the worker does not wear the safety helmet in time, the following modes are generally adopted:
1. the safety supervision personnel are arranged to conduct full-flow supervision on the crane operation on site, the manpower cost is high in this way, the safety cannot be ensured depending on the working attitude of the safety supervision personnel;
2. the safety helmet of the worker is simply monitored, whether the crane works or not cannot be identified, and the effect is poor;
3. the internet of things equipment is arranged on a person and a crane, so that hardware cost, deployment cost and use cost are high, and the charging is required to be disassembled, so that the system is inconvenient to use.
Disclosure of Invention
Based on the above, a crane operation alarming method, device, equipment and computer readable storage medium are provided for the technical problems.
The technical scheme adopted by the invention is as follows:
as a first aspect of the present invention, there is provided a crane operation warning method comprising:
acquiring pictures shot by a camera group, wherein the camera group comprises a plurality of cameras, and the cameras are arranged around a crane operation area to cover the crane operation area by 360 degrees;
detecting whether a crane target in a working state exists in the picture;
if the frames shot by any camera of the camera group have crane targets in working states, further detecting whether the frames shot by all cameras of the camera group have the person targets without the safety helmet;
and if the pictures shot by the cameras have the person targets without the safety helmet, carrying out alarm processing.
As a second aspect of the present invention, there is provided a crane operation warning device comprising:
the image acquisition module is used for acquiring images shot by the camera group, wherein the camera group comprises a plurality of cameras, and the cameras are arranged around a crane operation area to cover the crane operation area by 360 degrees;
the first detection module is used for detecting whether a crane target in a working state exists in the picture or not;
the second detection module is used for further detecting whether the picture shot by each camera of the camera group has a person target without a safety helmet or not if the picture shot by any camera of the camera group has a crane target in a working state;
and the alarm module is used for carrying out alarm processing if the images shot by the cameras have the person targets without the safety helmet.
As a third aspect of the present invention, there is provided an electronic device comprising a storage module including instructions loaded and executed by a processor, which when executed, cause the processor to perform a crane operation alerting method of the first aspect described above.
As a fourth aspect of the present invention, there is provided a computer-readable storage medium storing one or more programs which, when executed by a processor, implement a crane operation alerting method of the first aspect.
According to the invention, based on the cameras of the camera group, the plurality of cameras can cover the crane operation area in 360 degrees, when the crane is detected to work in the picture of any one camera, the picture of all the cameras is used for identifying whether the people target without the safety helmet is arranged in the picture, so that the problem of missing identification of a single camera is avoided, for example, when people in the picture of a certain camera are shielded by the crane, the picture of other cameras is used for identifying whether the people target without the safety helmet is arranged in the picture of the other cameras, the accuracy is high, the safety of crane operation is improved, and the mode is low in cost and convenient to deploy and use.
Drawings
The invention is described in detail below with reference to the attached drawings and detailed description:
FIG. 1 is a flow chart of a crane operation alarming method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a crane operation warning device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention;
fig. 4 is a schematic layout view of a plurality of cameras according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described below with reference to the drawings. The embodiments described in the present specification are not intended to be exhaustive or to represent the only embodiments of the present invention. The following examples are presented for clarity of illustration of the invention of the present patent and are not intended to limit the embodiments thereof. It will be apparent to those skilled in the art that various changes and modifications can be made in the embodiment described, and that all the obvious changes or modifications which come within the spirit and scope of the invention are deemed to be within the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides a crane operation alarming method, including:
s101, acquiring pictures shot by a camera group, wherein the camera group comprises a plurality of cameras 3, and the cameras 3 are arranged around a crane operation area to cover the crane operation area by 360 degrees, and see FIG. 4.
S102, detecting whether a crane target in a working state exists in a picture.
And S103, if the picture shot by any camera 3 of the camera group has a crane target in an operating state, further detecting whether the picture shot by each camera 3 of the camera group has a person target without a safety helmet.
The detection of whether the picture has a crane target in a working state or not and whether the picture has a person target without a safety helmet or not are realized through a deep learning detection model.
In this embodiment, the deep learning detection model adopts a yolov7 model, and the training process is as follows:
1. training sample acquisition:
a. the method comprises the steps of collecting videos of various crane indoor operations and unmanned, videos of various crane indoor operations and people wearing safety caps, videos of various crane indoor operations and people not wearing safety caps, videos of various crane outdoor operations and unmanned, videos of various crane outdoor operations and people wearing safety caps, videos of various crane outdoor operations and people not wearing safety caps, videos of various crane indoor non-operations and unmanned, videos of various crane indoor non-operations and people wearing safety caps, videos of various crane indoor non-operations and people not wearing safety caps, videos of various crane outdoor non-operations and people wearing safety caps and videos of various crane outdoor non-operations and people not wearing safety caps.
b. And acquiring pictures from each video.
c. Each picture is labeled with the following label using tools such as labelmg:
a crane_working label, namely the suspension arm is completely opened, and the suspension arm is marked;
crane_working1 tag: the suspension arm is completely opened, and the complete suspension arm and crane are marked;
crane_working_other tag: the crane which cannot be distinguished whether to work is marked, the crane body is only in the camera picture when the crane body is relatively close to the camera, and the crane is difficult to see clearly, so that whether to work can not be distinguished at the same time. Therefore, by setting the blank_working_other tag, the situation that whether the working condition cannot be distinguished can be filtered out during detection, so that error identification is avoided, and the accuracy is improved.
Crane_nonworking tag: labeling a crane with a non-opened suspension arm;
person tag: labeling people;
a helmet tag: the person wearing the safety helmet is marked, and the setting of the tag can avoid the error of identifying the person wearing the safety helmet as the person wearing the safety helmet by the optical head or white hair, so that the accuracy is improved.
head label, labeling people who wear no safety helmet.
2. And inputting the marked picture into a deep learning detection model for training.
The trained model represents crane targets with working states in the picture when the trained model identifies targets with crane working labels or crane working labels from the picture, and represents person targets without safety caps in the picture when the trained model identifies targets with head labels from the picture.
And S104, if the images shot by the cameras 3 have the person targets without the safety helmet, carrying out alarm processing.
As can be seen from the above, the method of the embodiment is based on the plurality of cameras of the camera group, the plurality of cameras can cover the crane operation area in 360 degrees, when the crane is detected to be working in the picture of any one of the cameras, the picture of all the cameras is used for identifying whether the people target without the safety helmet is worn or not, so that the problem of missing identification of a single camera is avoided, for example, when people in the picture of a certain camera are blocked by the crane, the picture of other cameras is used for identifying whether the people target without the safety helmet is worn or not, the accuracy is high, the safety of crane operation is improved, and the mode is low in cost and convenient to deploy and use.
A crane operation warning device according to one or more embodiments of the present invention will be described in detail below. Those skilled in the art will appreciate that these alerting means may be configured using commercially available hardware components through the steps taught by the present scheme. Fig. 2 shows a crane operation warning device according to an embodiment of the present invention, and as shown in fig. 2, the warning device includes a frame acquisition module 11, a first detection module 12, a second detection module 13, and a warning module 14.
The image acquisition module 11 is configured to acquire an image captured by a camera group, where the camera group includes a plurality of cameras 3, and the plurality of cameras 3 are disposed around the crane operation area to cover the crane operation area by 360 degrees.
The first detection module 12 is configured to detect whether the frame has a crane target in an operating state.
The second detection module 13 is configured to further detect whether the frames captured by the cameras 3 of the camera group have a person target without a helmet if the frames captured by any one of the cameras 3 of the camera group have a crane target in an operating state.
The detection of whether the picture has a crane target in a working state or not and whether the picture has a person target without a safety helmet or not are realized through a deep learning detection model.
In this embodiment, the deep learning detection model adopts a yolov7 model, and the training process is as follows:
1. training sample acquisition:
a. the method comprises the steps of collecting videos of various crane indoor operations and unmanned, videos of various crane indoor operations and people wearing safety caps, videos of various crane indoor operations and people not wearing safety caps, videos of various crane outdoor operations and unmanned, videos of various crane outdoor operations and people wearing safety caps, videos of various crane outdoor operations and people not wearing safety caps, videos of various crane indoor non-operations and unmanned, videos of various crane indoor non-operations and people wearing safety caps, videos of various crane indoor non-operations and people not wearing safety caps, videos of various crane outdoor non-operations and people wearing safety caps and videos of various crane outdoor non-operations and people not wearing safety caps.
b. And acquiring pictures from each video.
c. Each picture is labeled with the following label using tools such as labelmg:
a crane_working label, namely the suspension arm is completely opened, and the suspension arm is marked;
crane_working1 tag: the suspension arm is completely opened, and the complete suspension arm and crane are marked;
crane_working_other tag: the crane which cannot be distinguished whether to work is marked, the crane body is only in the camera picture when the crane body is relatively close to the camera, and the crane is difficult to see clearly, so that whether to work can not be distinguished at the same time. Therefore, by setting the blank_working_other tag, the situation that whether the working condition cannot be distinguished can be filtered out during detection, so that error identification is avoided, and the accuracy is improved.
Crane_nonworking tag: labeling a crane with a non-opened suspension arm;
person tag: labeling people;
a helmet tag: the person wearing the safety helmet is marked, and the setting of the tag can avoid the error of identifying the person wearing the safety helmet as the person wearing the safety helmet by the optical head or white hair, so that the accuracy is improved.
head label, labeling people who wear no safety helmet.
2. And inputting the marked picture into a deep learning detection model for training.
The trained model represents crane targets with working states in the picture when the trained model identifies targets with crane working labels or crane working labels from the picture, and represents person targets without safety caps in the picture when the trained model identifies targets with head labels from the picture.
And the alarm module 14 is used for carrying out alarm processing if the images shot by the cameras 3 have the person targets without the safety helmet.
In summary, the crane operation warning device provided in the above embodiments may perform the crane operation warning method provided in the above embodiments.
Similar to the above concept, the structure of the crane operation warning device shown in fig. 2 may be implemented as an electronic device, and fig. 3 is a schematic block diagram of the structure of the electronic device according to the embodiment of the present invention.
Illustratively, the electronic equipment includes a memory module 21 and a processor 22, the memory module 21 including instructions loaded and executed by the processor 22, which when executed, cause the processor 22 to perform the steps according to various exemplary embodiments of the present invention described in the above-described one of the crane operation alerting methods section of this specification.
It should be appreciated that the processor 22 may be a central processing unit (CentralProcessingUnit, CPU), and that the processor 22 may also be other general purpose processors, digital signal processors
(DigitalSignalProcessor, DSP) application specific Integrated Circuit
(ApplicationSpecificIntegratedCircuit, ASIC), field programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention also provide a computer-readable storage medium storing one or more programs that, when executed by a processor, implement the steps described in the foregoing description of one of the crane operation alerting methods according to various exemplary embodiments of the present invention.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer-readable storage media, which may include computer-readable storage media (or non-transitory media) and communication media (or transitory media).
The term computer-readable storage medium includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
By way of example, the computer readable storage medium may be an internal storage unit of the electronic device of the foregoing embodiments, such as a hard disk or a memory of the electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk provided on the electronic device, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash memory card (FlashCard), or the like.
The electronic device and the computer readable storage medium provided in the foregoing embodiments, based on the plurality of cameras of the camera group, the plurality of cameras can cover the crane operation area by 360 degrees, when the crane is detected to be operating in the picture of any one of the cameras, the picture of all the cameras is used to identify whether the people target without the safety helmet is present therein, so as to avoid the problem of missing identification of a single camera, for example, when the people in the picture of a certain camera is blocked by the crane, the picture of other cameras is used to identify whether the people target without the safety helmet is present therein, so that the accuracy is high, the safety of the crane operation is improved, and the mode is low in cost, and convenient to deploy and use.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. A crane operation warning method, comprising:
acquiring pictures shot by a camera group, wherein the camera group comprises a plurality of cameras, and the cameras are arranged around a crane operation area to cover the crane operation area by 360 degrees;
detecting whether a crane target in a working state exists in the picture;
if the frames shot by any camera of the camera group have crane targets in working states, further detecting whether the frames shot by all cameras of the camera group have the person targets without the safety helmet;
and if the pictures shot by the cameras have the person targets without the safety helmet, carrying out alarm processing.
2. The crane operation warning method according to claim 1, wherein the detecting whether the crane object has an operating state in the screen further comprises:
and detecting whether the picture has a crane target in a working state or not through a deep learning detection model.
3. The crane operation warning method according to claim 2, wherein the detecting whether the picture shot by each camera of the camera group has a person target without a helmet, further comprises:
and detecting whether the image has a person target without a safety helmet or not through the deep learning detection model.
4. A crane operation warning method according to claim 3, wherein the deep learning detection model is trained by:
training sample acquisition:
a. collecting videos of various crane indoor operations and unmanned, videos of various crane indoor operations and people wearing safety caps, videos of various crane indoor operations and people not wearing safety caps, videos of various crane outdoor operations and unmanned, videos of various crane outdoor operations and people wearing safety caps, videos of various crane outdoor operations and people not wearing safety caps, videos of various crane indoor operations and people not wearing safety caps, videos of various crane outdoor operations and people not wearing safety caps;
b. acquiring pictures from each video;
c. each picture is labeled with the following label:
a crane_working label, namely the suspension arm is completely opened, and the suspension arm is marked;
crane_working1 tag: the suspension arm is completely opened, and the complete suspension arm and crane are marked;
crane_working_other tag: labeling a crane which cannot distinguish whether to work or not;
crane_nonworking tag: labeling a crane with a non-opened suspension arm;
person tag: labeling people;
a helmet tag: labeling a person wearing the safety helmet;
head label, labeling people who wear no safety helmet.
And inputting the marked picture into the deep learning detection model for training.
5. The crane operation warning method of claim 4 wherein the deep learning detection model employs a yolov7 model.
6. A crane operation warning device, comprising:
the image acquisition module is used for acquiring images shot by the camera group, wherein the camera group comprises a plurality of cameras, and the cameras are arranged around a crane operation area to cover the crane operation area by 360 degrees;
the first detection module is used for detecting whether a crane target in a working state exists in the picture or not;
the second detection module is used for further detecting whether the picture shot by each camera of the camera group has a person target without a safety helmet or not if the picture shot by any camera of the camera group has a crane target in a working state;
and the alarm module is used for carrying out alarm processing if the images shot by the cameras have the person targets without the safety helmet.
7. An electronic device comprising a memory module including instructions loaded and executed by a processor, which when executed, cause the processor to perform a crane operation alerting method according to any one of claims 1-5.
8. A computer readable storage medium storing one or more programs, which when executed by a processor, implement a crane operation warning method according to any one of claims 1 to 5.
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| CN202211606875.7A CN116425047A (en) | 2022-12-13 | 2022-12-13 | Crane operation alarming method, device, equipment and computer readable storage medium |
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| CN202211606875.7A CN116425047A (en) | 2022-12-13 | 2022-12-13 | Crane operation alarming method, device, equipment and computer readable storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117068976A (en) * | 2023-08-04 | 2023-11-17 | 山东高速建设管理集团有限公司 | Crane construction standard safety detection method |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117068976A (en) * | 2023-08-04 | 2023-11-17 | 山东高速建设管理集团有限公司 | Crane construction standard safety detection method |
| CN117068976B (en) * | 2023-08-04 | 2024-05-03 | 山东高速建设管理集团有限公司 | Crane construction standard safety detection method |
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