CN114983080A - Intelligent safety helmet - Google Patents
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- CN114983080A CN114983080A CN202210560271.7A CN202210560271A CN114983080A CN 114983080 A CN114983080 A CN 114983080A CN 202210560271 A CN202210560271 A CN 202210560271A CN 114983080 A CN114983080 A CN 114983080A
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- 238000010276 construction Methods 0.000 claims abstract description 103
- 210000004556 brain Anatomy 0.000 claims abstract description 70
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000004891 communication Methods 0.000 claims abstract description 7
- 230000009466 transformation Effects 0.000 claims description 32
- 239000007789 gas Substances 0.000 claims description 29
- 238000006243 chemical reaction Methods 0.000 claims description 22
- 238000007781 pre-processing Methods 0.000 claims description 9
- 238000000354 decomposition reaction Methods 0.000 claims description 8
- 238000012544 monitoring process Methods 0.000 claims description 7
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 5
- 231100001261 hazardous Toxicity 0.000 claims description 4
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 3
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 3
- 229910021529 ammonia Inorganic materials 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 3
- 229910000037 hydrogen sulfide Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 description 4
- 239000000779 smoke Substances 0.000 description 3
- 230000000391 smoking effect Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
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Classifications
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- A—HUMAN NECESSITIES
- A42—HEADWEAR
- A42B—HATS; HEAD COVERINGS
- A42B3/00—Helmets; Helmet covers ; Other protective head coverings
- A42B3/04—Parts, details or accessories of helmets
- A42B3/0406—Accessories for helmets
- A42B3/0433—Detecting, signalling or lighting devices
- A42B3/046—Means for detecting hazards or accidents
-
- A—HUMAN NECESSITIES
- A42—HEADWEAR
- A42B—HATS; HEAD COVERINGS
- A42B3/00—Helmets; Helmet covers ; Other protective head coverings
- A42B3/04—Parts, details or accessories of helmets
- A42B3/0406—Accessories for helmets
- A42B3/042—Optical devices
-
- A—HUMAN NECESSITIES
- A42—HEADWEAR
- A42B—HATS; HEAD COVERINGS
- A42B3/00—Helmets; Helmet covers ; Other protective head coverings
- A42B3/04—Parts, details or accessories of helmets
- A42B3/0406—Accessories for helmets
- A42B3/0433—Detecting, signalling or lighting devices
- A42B3/0466—Means for detecting that the user is wearing a helmet
-
- A—HUMAN NECESSITIES
- A42—HEADWEAR
- A42B—HATS; HEAD COVERINGS
- A42B3/00—Helmets; Helmet covers ; Other protective head coverings
- A42B3/04—Parts, details or accessories of helmets
- A42B3/30—Mounting radio sets or communication systems
- A42B3/303—Communication between riders or passengers
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
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- Helmets And Other Head Coverings (AREA)
Abstract
The invention relates to an intelligent safety helmet, comprising: the dangerous gas detection module is arranged on the safety helmet body and used for detecting the concentration of dangerous gas in the construction environment; when the concentration of the dangerous gas exceeds a preset value, a gas alarm signal is sent out; the brain wave detection module is arranged on the safety helmet body and used for detecting the brain waves of construction workers and sending out an alarm signal of the safety helmet which is not worn when the brain waves cannot be detected; the video acquisition module is arranged on the safety helmet body and used for acquiring construction images and sending the acquired construction images to the upper computer; the voice talkback module is arranged on one side of the safety helmet body and used for voice communication among construction workers. Compared with the prior art, the method and the system have the advantages that the dangerous gas detection module and the brain wave detection module are used for detecting the state of a worker on a construction site, and then alarm information is sent out based on the state of the worker, so that the safety of the worker can be guaranteed in real time.
Description
Technical Field
The invention relates to the technical field of subway construction, in particular to an intelligent safety helmet.
Background
In the engineering field, wearable equipment of intelligence especially intelligent safety helmet also receives more and more attention, and these intelligent safety helmets can provide effects such as image acquisition, speech communication, position are confirmed and the navigation for the wearer to when being traditional safety helmet protection wearer's safety, also provide multiple auxiliary function for the wearer.
However, in a subway construction site, various dangerous gases may exist in the air, and when construction workers perform construction, the dangerous gases are hardly paid attention to by the construction workers without accumulating to a certain concentration, but when the dangerous gases accumulate to a certain concentration, the life safety of the construction workers is threatened. In addition, some construction workers often smoke and take off the safety helmet in the construction process, so that the life safety of the construction workers is threatened.
Disclosure of Invention
In order to solve the above problems, an embodiment of the present invention provides an intelligent safety helmet.
An intelligent safety helmet, comprising:
the dangerous gas detection module is arranged on the safety helmet body and used for detecting the concentration of dangerous gas in the construction environment; when the concentration of the dangerous gas exceeds a preset value, a gas alarm signal is sent out; the hazardous gas comprises any one or more of carbon monoxide, oxygen, ammonia and hydrogen sulfide;
the brain wave detection module is arranged on the safety helmet body and used for detecting the brain waves of construction workers and sending out an alarm signal of the safety helmet which is not worn when the brain waves are not detected;
the video acquisition module is arranged on the safety helmet body and used for acquiring construction images and sending the acquired construction images to the upper computer;
the voice talkback module is arranged on one side of the safety helmet body and used for voice communication among construction workers.
Preferably, the brain wave detecting module includes:
the wavelet decomposition unit is used for performing multi-scale wavelet transformation on the brain waves to obtain wavelet coefficients;
the brain wave denoising model building unit is used for building a brain wave denoising model according to the wavelet coefficients;
the threshold construction unit is used for constructing a denoising threshold according to the wavelet transform scale and the length of the brain wave signal under the corresponding transform scale;
the denoising unit is used for denoising the brain waves by using the denoising threshold and the brain wave denoising model to obtain denoised brain waves;
and the brain wave monitoring unit is used for monitoring the de-noised brain waves and sending out an alarm signal of the unworn safety helmet when the brain waves are not detected.
Preferably, the brain wave denoising model constructing unit includes:
the brain wave denoising model construction subunit is used for adopting a formula:
constructing a brain wave denoising model; wherein, w ij Representing the jth wavelet coefficient, T, at the ith transform scale i And representing a denoising threshold value under the ith transformation scale, wherein n is a preset coefficient.
Preferably, the threshold constructing unit includes:
the brain wave denoising threshold value construction subunit is used for adopting a formula:
constructing a denoising threshold; wherein, T i Representing a denoising threshold value under the ith transformation scale, R representing an adjustable parameter, i representing the scale of wavelet transformation, mu representing the signal length under the ith transformation scale, and sigma representing the standard deviation of a wavelet coefficient under the ith decomposition scale, and mean (| w i,j L) represents the median value, σ, of the wavelet coefficients at the ith transform scale 0 ＝median(|w i,j |)/0.6745。
Preferably, the video capture module includes:
the video preprocessing module is used for preprocessing the construction image to obtain a preprocessed construction image;
the image enhancement module is used for carrying out image enhancement on the preprocessed construction image to obtain an enhanced construction image;
and the image sending module is used for sending the enhanced construction image to an upper computer.
Preferably, the video pre-processing module includes:
the neighborhood determining unit is used for taking a neighborhood by taking each pixel point in the construction image as a center;
the image denoising unit is used for taking the average gray value of the neighborhood as the output of the corresponding pixel point to obtain the preprocessed construction image, wherein the output formula of the pixel point is as follows:
g (j, k) represents the output value of the pixel point (j, k), N multiplied by N represents the size of the neighborhood, A represents the point set formed by the neighborhood pixels, and d (m, N) represents the gray value of the pixel point on the neighborhood.
Preferably, the image enhancement module comprises:
the gray value conversion unit is used for constructing a gray value conversion function according to the gray value of each pixel point of the preprocessed construction image;
the gray value conversion unit is used for constructing a gray value clustering conversion function according to the gray value conversion function;
the gray level enhancement unit is used for carrying out gray level transformation on the preprocessed construction image by utilizing a gray level clustering transformation function to obtain a gray level clustering value;
and the gray level conversion unit is used for converting the gray level cluster value into the enhanced construction image by using an image conversion function.
Preferably, the gray value conversion function is:
wherein, X m,n Representing the gray value, X, of the pixel point of the construction image at (m, n) max Representing the maximum grey value, X, on the construction image min Representing the minimum gray value on the construction image and d representing the adjustable threshold.
Preferably, the gray value cluster transformation function is:
wherein Y' represents the gray level cluster value of any point on the construction image after conversion.
Preferably, the image transfer function is:
wherein f is E And the gray value of any point on the construction image after conversion is shown.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to an intelligent safety helmet, comprising: the dangerous gas detection module is arranged on the safety helmet body and used for detecting the concentration of dangerous gas in the construction environment; when the concentration of the dangerous gas exceeds a preset value, a gas alarm signal is sent out; the brain wave detection module is arranged on the safety helmet body and used for detecting the brain waves of construction workers and sending out an alarm signal of the safety helmet which is not worn when the brain waves are not detected; the video acquisition module is arranged on the safety helmet body and used for acquiring construction images and sending the acquired construction images to the upper computer; the voice talkback module is arranged on one side of the safety helmet body and used for voice communication among construction workers. Compared with the prior art, the method and the system have the advantages that the dangerous gas detection module and the brain wave detection module are used for detecting the state of a worker on a construction site, and then alarm information is sent out based on the state of the worker, so that the safety of the worker can be guaranteed in real time.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an intelligent safety helmet in an embodiment provided by the present invention.
Detailed Description
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are used merely for convenience of description and simplification of the description, but do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The embodiment of the invention aims to provide an intelligent safety helmet which is used for guaranteeing the safety of construction workers in real time.
Referring to fig. 1, an intelligent helmet includes: the system comprises a hazardous gas detection module, a brain wave detection module, a video acquisition module 2 and a voice talkback module 3.
The dangerous gas detection module is arranged on the safety helmet body 1 and used for detecting the concentration of dangerous gas in the construction environment; when the concentration of the dangerous gas exceeds a preset value, a gas alarm signal is sent out; the hazardous gas comprises any one or more of carbon monoxide, oxygen, ammonia and hydrogen sulfide; the brain wave detection module is arranged on the inner side of the safety helmet body 1 and is used for detecting the brain waves of construction workers and sending an alarm signal of the safety helmet which is not worn when the brain waves cannot be detected; the video acquisition module 2 is arranged on the safety helmet body 1 and used for acquiring construction images and sending the acquired construction images to an upper computer; and the voice intercom module 3 is arranged on one side of the safety helmet body 1 and used for voice communication among construction workers.
Further, the brain wave detecting module includes:
the wavelet decomposition unit is used for performing multi-scale wavelet transformation on the brain waves to obtain wavelet coefficients;
wavelet transform algorithms are a common data processing technique. Wavelet coefficients with different sizes can be generated by transforming the brain waves through a certain scale, wherein the wavelet coefficients of the brain waves without noise are in direct proportion to the decomposition scale, and the noise is in inverse proportion to the decomposition scale, so that the noise elimination processing of the brain waves can be realized based on the characteristic.
The brain wave denoising model building unit is used for building a brain wave denoising model according to the wavelet coefficients; the brain wave denoising model construction subunit is used for adopting a formula:
constructing a brain wave denoising model; wherein, w ij Representing the jth wavelet coefficient, T, at the ith transform scale i And representing a denoising threshold value under the ith transformation scale, wherein n is a preset coefficient.
The threshold construction unit is used for constructing a denoising threshold according to the wavelet transform scale and the length of the brain wave signal under the corresponding transform scale; wherein, threshold value construction unit includes:
the brain wave denoising threshold value constructing subunit is used for adopting a formula:
constructing a denoising threshold; wherein, T i Representing a denoising threshold value under the ith transformation scale, R representing an adjustable parameter, i representing the scale of wavelet transformation, mu representing the signal length under the ith transformation scale, and sigma representing the standard deviation of a wavelet coefficient under the ith decomposition scale, and mean (| w i,j L) represents the median value, σ, of the wavelet coefficients at the ith transform scale 0 ＝median(|w i,j |)/0.6745。
In the wavelet threshold denoising process, the quality of threshold selection directly influences the denoising effect. The adaptive threshold is constructed based on the standard deviation of the wavelet coefficients under different transformation scales, the adaptive threshold transformation can be carried out on the wavelet coefficients under each scale, the detail characteristics of different scales are highlighted, and the noise of the image is suppressed.
The denoising unit is used for denoising the brain waves by using the denoising threshold and the brain wave denoising model to obtain denoised brain waves;
and the brain wave monitoring unit is used for monitoring the de-noised brain waves and sending out an alarm signal of the unworn safety helmet when the brain waves are not detected.
The invention can prevent the occurrence of false alarm caused by the interference of external electric waves by monitoring the de-noised brain waves.
In an embodiment of the present invention, the video capture module 2 includes:
the video preprocessing module is used for preprocessing the construction image to obtain a preprocessed construction image;
further, the video preprocessing module includes:
the neighborhood determining unit is used for taking a neighborhood by taking each pixel point in the construction image as a center;
the image denoising unit is used for taking the average gray value of the neighborhood as the output of the corresponding pixel point to obtain the preprocessed construction image, wherein the output formula of the pixel point is as follows:
wherein, G (j, k) represents the output value of the pixel point (j, k), N multiplied by N represents the size of the neighborhood, A represents the point set formed by the neighborhood pixels, and d (m, N) represents the gray value of the pixel point on the neighborhood.
According to the method, one neighborhood is selected, the gray average value of all pixels in the neighborhood is calculated and is used as the output of the central pixel, so that the noise in the construction image can be effectively eliminated, and the construction image is smoother.
The image enhancement module is used for carrying out image enhancement on the preprocessed construction image to obtain an enhanced construction image;
in an embodiment of the present invention, an image enhancement module includes:
the gray value conversion unit is used for constructing a gray value conversion function according to the gray value of each pixel point of the preprocessed construction image;
the gray value transfer function is:
wherein X m,n Representing the gray value, X, of the pixel point of the construction image at (m, n) max Representing the maximum grey value, X, on the construction image min Representing the minimum gray value on the construction image, d representing an adjustable threshold, which is generally preferred [1,2]。
The gray value transformation unit is used for constructing a gray value clustering transformation function according to the gray value transformation function; the gray value clustering transformation function is as follows:
wherein Y' represents the gray level cluster value of any point on the construction image after conversion.
The gray level enhancement unit is used for carrying out gray level transformation on the preprocessed construction image by utilizing a gray level clustering transformation function to obtain a gray level clustering value;
and the gray level conversion unit is used for converting the gray level cluster value into the enhanced construction image by using an image conversion function. Wherein. The image transfer function is:
wherein, f E And the gray value of any point on the construction image after transformation is represented.
According to the invention, the image is enhanced by using the image conversion function, so that the contrast between all the characteristics on the construction image can be improved, and the construction image is clearer.
And the image sending module is used for sending the enhanced construction image to an upper computer.
After the enhanced construction image is sent to the upper computer, the enhanced construction image needs to be input into the smoking early warning neural network model, whether a field construction worker smokes smoke is judged by using the smoking early warning neural network model, and when people smoke is found, the upper computer can send an alarm to the smoking worker through the voice talkback module 3, so that the occurrence of subsequent accidents is avoided.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to an intelligent safety helmet, comprising: the dangerous gas detection module is arranged on the safety helmet body and used for detecting the concentration of dangerous gas in the construction environment; when the concentration of the dangerous gas exceeds a preset value, a gas alarm signal is sent out; the brain wave detection module is arranged on the safety helmet body and used for detecting the brain waves of construction workers and sending out an alarm signal of the safety helmet which is not worn when the brain waves are not detected; the video acquisition module is arranged on the safety helmet body and used for acquiring construction images and sending the acquired construction images to the upper computer; the voice talkback module is arranged on one side of the safety helmet body and used for voice communication among construction workers. Compared with the prior art, the method and the system have the advantages that the dangerous gas detection module and the brain wave detection module are used for detecting the state of a worker on a construction site, and then alarm information is sent out based on the state of the worker, so that the safety of the worker can be guaranteed in real time.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and the present invention shall be covered by the claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An intelligent safety helmet, comprising:
the dangerous gas detection module is arranged on the safety helmet body and used for detecting the concentration of dangerous gas in the construction environment; when the concentration of the dangerous gas exceeds a preset value, a gas alarm signal is sent out; the hazardous gas comprises any one or more of carbon monoxide, oxygen, ammonia and hydrogen sulfide;
the brain wave detection module is arranged on the safety helmet body and used for detecting the brain waves of construction workers and sending out an alarm signal of the safety helmet which is not worn when the brain waves are not detected;
the video acquisition module is arranged on the safety helmet body and used for acquiring construction images and sending the acquired construction images to the upper computer;
the voice talkback module is arranged on one side of the safety helmet body and used for voice communication among construction workers.
2. The intelligent safety helmet of claim 1, wherein the brain wave detection module comprises:
the wavelet decomposition unit is used for performing multi-scale wavelet transformation on the brain waves to obtain wavelet coefficients;
the brain wave denoising model building unit is used for building a brain wave denoising model according to the wavelet coefficients;
the threshold construction unit is used for constructing a denoising threshold according to the wavelet transform scale and the length of the brain wave signal under the corresponding transform scale;
the denoising unit is used for denoising the brain waves by using the denoising threshold and the brain wave denoising model to obtain denoised brain waves;
and the brain wave monitoring unit is used for monitoring the de-noised brain waves and sending out an alarm signal of the unworn safety helmet when the brain waves are not detected.
3. The intelligent safety helmet according to claim 2, wherein the brain wave denoising model constructing unit comprises:
the brain wave denoising model construction subunit is used for adopting a formula:
constructing a brain wave denoising model; wherein, w ij Representing the jth wavelet coefficient, T, at the ith transform scale i Representing the de-noising threshold at the ith transform scale, nIs a preset coefficient.
4. The intelligent safety helmet of claim 3, wherein the threshold building unit comprises:
the brain wave denoising threshold value constructing subunit is used for adopting a formula:
constructing a denoising threshold; wherein, T i Representing a denoising threshold value under the ith transformation scale, R representing an adjustable parameter, i representing the scale of wavelet transformation, mu representing the signal length under the ith transformation scale, and sigma representing the standard deviation of a wavelet coefficient under the ith decomposition scale, and mean (| w i,j L) represents the median value, σ, of the wavelet coefficients at the ith transform scale 0 ＝median(|w i,j |)/0.6745。
5. The intelligent safety helmet of claim 1, wherein the video capture module comprises:
the video preprocessing module is used for preprocessing the construction image to obtain a preprocessed construction image;
the image enhancement module is used for carrying out image enhancement on the preprocessed construction image to obtain an enhanced construction image;
and the image sending module is used for sending the enhanced construction image to an upper computer.
6. The intelligent safety helmet of claim 5, wherein the video pre-processing module comprises:
the neighborhood determining unit is used for taking a neighborhood by taking each pixel point in the construction image as a center;
the image denoising unit is used for taking the average gray value of the neighborhood as the output of the corresponding pixel point to obtain the preprocessed construction image, wherein the output formula of the pixel point is as follows:
wherein, G (j, k) represents the output value of the pixel point (j, k), N multiplied by N represents the size of the neighborhood, A represents the point set formed by the neighborhood pixels, and d (m, N) represents the gray value of the pixel point on the neighborhood.
7. The smart headgear of claim 6, wherein the image enhancement module comprises:
the gray value conversion unit is used for constructing a gray value conversion function according to the gray value of each pixel point of the preprocessed construction image;
the gray value transformation unit is used for constructing a gray value clustering transformation function according to the gray value transformation function;
the gray level enhancement unit is used for carrying out gray level transformation on the preprocessed construction image by utilizing a gray level clustering transformation function to obtain a gray level clustering value;
and the gray level conversion unit is used for converting the gray level cluster value into the enhanced construction image by using an image conversion function.
8. The intelligent safety helmet of claim 7, wherein the gray value transfer function is:
wherein, X m,n Representing the gray value, X, of the pixel point of the construction image at (m, n) max Representing the maximum grey value, X, on the construction image min Representing the minimum gray value on the construction image and d representing the adjustable threshold.
9. The intelligent safety helmet of claim 8, wherein the gray value clustering transformation function is:
wherein Y' represents the gray level cluster value of any point on the construction image after conversion.
10. The intelligent safety helmet of claim 9, wherein the image transfer function is:
wherein, f E And the gray value of any point on the construction image after conversion is shown.
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