AU2021100176A4 - A Monitoring Method of Seagoing Vessels Exhaust Emission by Smart Phones - Google Patents
A Monitoring Method of Seagoing Vessels Exhaust Emission by Smart Phones Download PDFInfo
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- 238000003672 processing method Methods 0.000 claims abstract description 5
- 238000002360 preparation method Methods 0.000 claims abstract description 3
- 241000845077 Iare Species 0.000 claims description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/17—Image acquisition using hand-held instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
- G01N2021/177—Detector of the video camera type
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/0011—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement
- G05D1/0038—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement by providing the operator with simple or augmented images from one or more cameras located onboard the vehicle, e.g. tele-operation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30232—Surveillance
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
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Abstract
The invention discloses a monitoring method for the exhaust emission of seagoing vessels by
using smart phones, which comprises the following steps. Si. Navigation video collection of
seagoing vessels suspected of emissions violations by using the smart phone. S2. Location
tracking of seagoing vessels in other images based on the positions of the seagoing vessels in
the first frame image. S3. Preparation of Ringelmann colorimetric card for evaluating the
blackness of tail gas in each frame image. S4. Determination of blackness of seagoing vessel
exhaust gas. The same image processing method as S3 is used to process all the pixels of the
exhaust region in each frame to extract the luminance value of the representative pixels, which
is then compared with the Ringelmann colorimetric card. S5. Judgment on whether the
seagoing vessel tail gas is discharged illegally. This method can utilize the video taken by
maritime law enforcement officers in the non-stationary state of platform, hands and targets to
detect the illegal exhaust emission seagoing vessels. Further, it can realize automatic tracking
of suspected illegal seagoing vessels and quantitatively judge whether the discharged black
smoke reaches the illegal degree, which is convenient to operate, efficient and accurate.
-1/2
Manual assistant aiming "Inforn" the procedure of
at chimney mouth and vessel hull parts and exhaust
recording video gas parts
AutoatictracingAvoid the interference
AAtomaticcustaackmg
vesselcaused by the movement of
platform, hands and target
Making virtual Automatic determination
Ringelmann of no exhaust gas and
colorimetric card virtual darkest exhaust gas
Id IFyh Automatic comparison of
Ringelmann - exhaust gas
and virtual
blackness level Ringelmain
colorimetric
card
Figure 1
Figure 2
Figure 3
Description
-1/2
Manual assistant aiming "Inforn" the procedure of at chimney mouth and vessel hull parts and exhaust recording video gas parts
AutoatictracingAvoid the interference vesselcaused by the movement of platform, hands and target
Making virtual AAtAutomatic omaticcustaackmg determination Ringelmann of no exhaust gas and colorimetric card virtual darkest exhaust gas
Id IFyh Automatic comparison of
Ringelmann - exhaust gas and virtual blackness level Ringelmain colorimetric card
Figure 1
Figure 2
Figure 3
A Monitoring Method of Seagoing Vessels Exhaust Emission by Smart Phones
The invention relates to the technical field of capturing mobile pollution source images, in
particular to a monitoring method of seagoing vessels exhaust emission by smart phones.
At present, the vessel exhaust gas is the main treatment object of the "Blue Sky Protection
Campaign". Specifically, the black smoke from vessels left the worst impression on the
residents of coastal and river cities. In 2018, Shanghai took the lead in requiring "vehicles
and vessels shall not emit visible black smoke". It has been widely used to judge the black smoke
level of fixed pollution sources (steel plants, power plants, etc.) by putting the Ringelmann blackness
colorimetric card into the camera lens as Ringelmann blackness. However, this method is not suitable for the
complex environment with mobile platform (coastal patrol vessel) and moving object (vessel). At present,
the method of capturing the seagoing vessels emitting black smoke is that the law enforcement officers record
a certain length of video (the short-term black smoke caused by sudden acceleration of the vessel is not
illegal), and carry a Ringelmann blackness colorimetric card to judge whether the black smoke degree
exceeds level II or above; however, this method has a strong subjectivity in determining the degree of black
smoke, which is easy to cause law enforcement disputes.
The purpose of the invention is to provide a method of using smart phones to realize the
monitoring of seagoing vessels exhaust emission based on the image acquisition method
which requires the patrol platform, human hands and image acquisition target are in non
static state, so as to effectively monitor and judge whether there is violation emission.
Therefore, the technical scheme of the invention is as follows:
A monitoring method for the exhaust emission of seagoing vessels by using smart phones
includes the following steps.
Si. The law enforcement officers shoot the navigation video of the seagoing vessels
suspected of illegal emission through the smart phones, so as to obtain multiple images of
the suspected seagoing vessels. Wherein, in the video acquisition process, the smart phones
should always maintain the state as the first image acquisition.
S2. Based on the position information of the suspected seagoing vessel in the first image,
the remaining images obtained in S Iare processed in order to determine the position
information of the seagoing vessel in the remaining images. The specific treatment process
is as follows:
S201. A plane image coordinate system is established to describe the target area in the first
frame image by taking the row number of pixels in the image as ordinate x and the column
number as abscissa y. Specifically, the target area is a rectangular frame, divided into four
subregion: upper left, lower left, upper right and lower right. The centre point of the
rectangular frame is aligned with the chimney mouth of the target seagoing vessel, so that
the hull of the ship is located in one subregion, that is, hull subregion. Besides, the exhaust
gas is located in another subregion, that is, the exhaust subregion. Based on this, the
location information of the target area can be described as: [xi ~ xj, ym ~ yn], wherein, xi
and xj are the starting and ending row numbers of the rectangular region, j > i; ym and yn
are the starting and ending column numbers of the rectangular region, n > m. The total
pixel number of the target region z = (j-i+1) x (n-m+1).
S202. Using the edge operator of the hull subregion in the first frame image to highlight
the edge pixels in the region, and based on the position information of the hull subregion
in the first frame image- [Xa ~ Xb, yc ~ yd], b > a, d > c, the images of nine regions to be
selected are intercepted in the second frame image with position information as [Xa- ~ Xb
1, ye-1 ~ yd-1], [Xa-1 ~ Xb-1, ye ~ Yd], [Xa-1 ~ Xb-1, yc+1 ~ yd+1], [Xa ~ Xb, ye-1 ~ yd-1], [Xa~Xb, yc
yd], [Xa~Xb, yc+1~yd+1], [Xa+1~Xb+1, ye-l-yd-1], [Xa+1~Xb+1, ym-yd], [Xa+1~Xb+1, ym+1~
yd+1], respectively. Then the edge operator is used to process the images of nine regions to
highlight the edge pixels in the region.
S203. The luminance value of the hull subregion in the first frame image is calculated with
that of nine selected regions obtained from the second frame image, that is, the luminance
values of each pair of pixels with the same row and column number in each two regions
are subtracted and the absolute values are taken to obtain nine difference processing
images.
S204. The sum of all the pixel values in the nine difference processing images is obtained
respectively, and the difference processing image with the smallest sum result is selected.
The image of the selected region corresponding to the difference processing image is
determined as the hull subregion of the second frame image.
S205. Repeating the above S201 to S204, and the frame image is processed based on the
previous frame image of each frame image to obtain the hull subregion of the frame image,
thereby realizing the target seagoing vessel tracking.
The image acquisition and processing method of S2 solves the problems of the unstable
position of the chimney mouth in the picture caused by the swaying of the coastal patrol
vessel, the shaking of the photographer's hands and the moving of the target vessel in the current real-time video acquisition, and realizes the purpose of automatic tracking the target seagoing vessel in the video.
S3. All the pixels in the target area of each image obtained in S are processed and sorted
in order of luminance value. By extracting the luminance values of the representative
pixels, a Ringelmann colorimetric card for evaluating the blackness of exhaust gas in each
image is made.
S4. Using the same image processing method as making Ringelmann colorimetric cards in
S3 to process and sort all the pixels of the exhaust subregion in the target area of each frame
image obtained in Si in order of luminance value. By extracting the luminance values of
the representative pixels and comparing with the Ringelmann colorimetric card based on
the same image, the blackness of the seagoing vessel exhaust gas in the image is judged.
S5. Based on the running time of the seagoing vessel, the multi frame images of which
indicate continuous occurrence of illegal seagoing vessel exhaust blackness, whether there
is seagoing vessel with illegal exhaust emission is determined.
Further, in S3, the specific preparation method of Ringelmann colorimetric card is as
follows:
S301. The luminance values of all the pixels in the target area of the image are taken as the
processing objects, and the smallest luminance value among the blue luminance value
DNblue, green luminance value DNgreenand red luminance value DNred of each pixel is
selected as the grey value of the pixel.
S302. Taking the grey value as the abscissa and the pixel number as the ordinate, the grey
value histogram of all pixels is made. Further, in the grey value histogram, the grey value
of the M-th pixel ranking from small to large is defined as Ringelmann level 5 blackness, and the grey value of the M-th pixel ranking from large to small is defined as Ringelmann level 0 blackness.
S303. Based on the definition method of S302, correspondingly, the following is defined:
the grey value of Ringelmann level 1 blackness = the grey value of Ringelmann level 0
blackness x 80% + the grey value of Ringelmann level 5 blackness x 20%;
the grey value of Ringelmann level 2 blackness = the grey value of Ringelmann level 0
blackness x 60% + the grey value of Ringelmann level 5 blackness x 40%;
the grey value of Ringelmann level 3 blackness = the grey value of Ringelmann level 0
blackness x 40% + the grey value of Ringelmann level 5 blackness x 60%;
the grey value of Ringelmann level 4 blackness = the grey value of Ringelmann level 0
blackness x 20% + the grey value of Ringelmann level 5 blackness x 80%;
Further, in S302, the value of M is taken as 1% of the total pixel number in the target area
of the image.
Further, in S4, the specific implementation method for judging the blackness of seagoing
vessel exhaust gas in the image is as follows:
S401. The luminance values of all the pixels in the target area of the image are taken as the
processing objects, and the smallest luminance value among the blue luminance value
DNblue, green luminance value DNgreenand red luminance value DNred of each pixel is
selected as the grey value of the pixel.
S402. Taking the grey value as the abscissa and the pixel number as the ordinate, the grey
value histogram of all pixels is made. Further, in the grey value histogram, the grey value
of the N-th pixel ranking from small to large is defined as grey value of exhaust gas.
S403. The grey value of the exhaust gas obtained in S402 is compared with the
Ringelmann colorimetric card based on the same image, and the Ringelmann blackness
level closest to the grey value of the exhaust gas is taken as the Ringelmann blackness level
of the seagoing vessel exhaust gas in the image.
Further, in S402, the value of N is taken as 1% of the total pixel number in the target area
of the image.
Compared with the prior art, this method can utilize the video taken by maritime law
enforcement officers in the non-stationary state of platform, hands and targets to detect the
illegal exhaust emission seagoing vessels. Further, it can realize automatic tracking of
suspected illegal seagoing vessels and quantitatively judge whether the discharged black
smoke reaches the illegal degree, which is convenient to operate, efficient and accurate.
Figure 1 is the flow chart of monitoring method for the exhaust emission of seagoing
vessels by using smart phones of the present invention.
Figure 2 is a schematic diagram of the target area in the first frame image in S2 of the
embodiment of the present invention.
Figure 3 is the grey image of the target region in the first frame after edge operator
highlights edge pixels in the embodiment of the invention.
Figure 4 is the grey image of the target region part of the 75th frame image obtained in the
third second of video recording after edge operator highlights edge pixels in the
embodiment of the invention.
-'7
Figure 5 is the grey histogram of all pixels in the target area of the first frame image in the
embodiment of the invention after processing in S3.
Figure 6 is a grey image of the exhaust subregion in the target region of the first frame
image in the embodiment of the present invention after processing in S4.
The invention will be further described in combination with the attached figures and
specific embodiments, but the following embodiments are not limiting the invention in any
way.
Taking the video of a smoky vessel shot on the wharf of Zhenjiang Maritime Safety
Administration on May 25, 2020 at 14 PM as an example, the monitoring method of the
application is further explained. The day is cloudy, and the vessel is far away, so it is
difficult to visually identify the degree of black smoke, while this monitoring method can
still be used to effectively judge whether the vessel is discharging illegally.
As shown in Fig. 1, a monitoring method for the exhaust emission of seagoing vessels by
using smart phones is composed of the following steps.
S1. The law enforcement officers shot the navigation video of the seagoing vessels
suspected of illegal emission through the smart phones. Wherein, in the video acquisition
process, the smart phones should always maintain the state as the first image acquisition.
The length of the video was 16 s, including 480 frames of images; the total pixel number
in each frame is 921,600, the corresponding pixel number in each row is 1,280, and in each
column is 720.
S2. Based on the position information of the suspected seagoing vessel in the first image,
the remaining images obtained in Si were processed in order to determine the position
information of the seagoing vessel in the remaining images. The specific treatment process
was as follows:
S201. A plane image coordinate system was established to describe the target area in the
first frame image by taking the row number of pixels in the image as ordinate x and the
column number as abscissa y. In fig. 3, the target area (1) was a rectangular frame, divided
into four subregions: upper left, lower left, upper right and lower right. The centre point of
the rectangular frame was aligned with the chimney mouth of the target seagoing vessel,
so that the hull of the ship was located in lower left subregion (2) and the exhaust gas was
located in upper right subregion (3). Based on this, the location information of the target
area can be described as: [595 ~ 864, 261 ~ 468], wherein, 595 and 864 were the starting
and ending row numbers of the rectangular region; 261 and 468 were the starting and
ending column numbers of the rectangular region. The total pixel number of the target
region z = (j-i+1) x (n-m+1) = 56,160.
S202. Using the edge operator of the hull subregion in the first frame image to highlight
the edge pixels in the region, and based on the position information of the hull subregion
in the first frame image- [595 ~ 729, 365 ~ 468] the images of nine regions to be selected
are intercepted in the second frame image with position information as [594~728, 364~
467], [594~728, 365~468], [594~728, 366~469], [595~729, 364~467], [595~729,
365~468], [595~729, 366~469], [596~730, 364~467], [596~730, 365~468],
[596~-730, 366~469] respectively. Then the edge operator was used to process the images
of nine regions to highlight the edge pixels in the region.
S203. The luminance value of the hull subregion in the first frame image was calculated
with that of nine selected regions obtained from the second frame image, that was, the
luminance values of each pair of pixels with the same row and column number in each two
regions were subtracted and the absolute values were taken to obtain nine difference
processing images.
S204. The sum of all the pixel values in the nine difference processing images was obtained
respectively, and the difference processing image with the smallest sum result was selected.
The image of the selected region corresponding to the difference processing image was
determined as the hull subregion of the second frame image.
Fig. 3 is the grey image of the first frame image in the embodiment obtained after the edge
operator processing. From the edge pixels in Fig. 3, it can be seen that the automatic
tracking of the vessel mainly depends on the boundary between the vessel and the water,
and the texture inside the vessel remaining almost unchanged between the frames, while
the boundary between the remote forest belt and the sky was unreliable. Determined after
the processing in S2, fig. 4 is the grey image of the vessel subregion in the 75th frame
image obtained in the third second of video recording in the embodiment. The picture in
the target area of the 75th frame after the automatic vessel tracking algorithm runs for 3
seconds proves that the automatic vessel tracking scheme is effective and accurate.
S3. All the pixels in the target area of each image obtained in S were processed and sorted
in order of luminance value. By extracting the luminance values of the representative
pixels, a Ringelmann colorimetric card for evaluating the blackness of exhaust gas in each
image was made.
The specific steps of S3 were as follows:
S301. The luminance values of all the pixels in the target area of the image are taken as the
processing objects, and the smallest luminance value among the blue luminance value
DNblue, green luminance value DNgreenand red luminance value DNred of each pixel is
selected as the grey value of the pixel.
S302. Taking the grey value as the abscissa and the pixel number as the ordinate, the grey
value histogram of all pixels is made. As shown in fig. 5, in the grey value histogram, the
grey value of the 562nd pixel ranking from small to large is defined as Ringelmann level 5
blackness, and the grey value of the 562nd pixel ranking from large to small is defined as
Ringelmann level 0 blackness.
S303. Based on the definition method of S302, the grey value of Ringelmann level 5
blackness was 141 and the grey value of Ringelmann level 0 blackness was 217.
Correspondingly, the following was defined:
the grey value of Ringelmann level 1 blackness = the grey value of Ringelmann level 0
blackness x 80% + the grey value of Ringelmann level 5 blackness x 20% = 156.2
the grey value of Ringelmann level 2 blackness = the grey value of Ringelmann level 0
blackness x 60% + the grey value of Ringelmann level 5 blackness x 40% = 171.4
the grey value of Ringelmann level 3 blackness = the grey value of Ringelmann level 0
blackness x 40% + the grey value of Ringelmann level 5 blackness x 60% = 186.6
the grey value of Ringelmann level 4 blackness = the grey value of Ringelmann level 0
blackness x 20% + the grey value of Ringelmann level 5 blackness x 80%= 201.8
S4. Using the same image processing method as making Ringelmann colorimetric cards in
S3 to process and sort all the pixels of the exhaust subregion in the target area of each frame
image obtained in Si in order of luminance value. By extracting the luminance values of the representative pixels and comparing with the Ringelmann colorimetric card based on the same image, the blackness of the seagoing vessel exhaust gas in the image is judged.
The specific implementation method of S4 was as follows:
S401. The luminance values of all the pixels in the target area of the image were taken as
the processing objects, and the smallest luminance value among the blue luminance value
DNblue, green luminance value DNgreenand red luminance value DNred of each pixel was
selected as the grey value of the pixel.
S402. Taking the grey value as the abscissa and the pixel number as the ordinate, the grey
value histogram of all pixels was made. Further, in the grey value histogram, the grey value
of the 140th pixel ranking from small to large was defined as grey value of exhaust gas.
S403. The grey value of the exhaust gas obtained in S402 was compared with the
Ringelmann colorimetric card based on the same image, and the Ringelmann blackness
level closest to the grey value of the exhaust gas was taken as the Ringelmann blackness
level of the seagoing vessel exhaust gas in the image.
Specifically, as shown in fig. 6, the grey image of the exhaust subregion in the target region
of the first frame image obtained through S4, the region discrimination image was obtained
by comparing each pixel with the Ringelmann colorimetric card which was obtained on
basis of the first frame image. Wherein, the range of red frame is Ringelmann blackness
level 2 region, the range of blue frame is Ringelmann blackness level 1 region, and the
range outside blue frame is Ringelmann blackness level 0 region. In order to avoid the error
of a few end value pixels, in the forward order of the grey value corresponding to the first
frame image from small to large, the grey value of the 140th pixel- 175 3 was taken as the
grey value of exhaust gas. It was found that the grey value was between Ringelmann blackness level 2 (171.4) and Ringelmann blackness level 3 (186.6) when compared with the Ringelmann colorimetric card obtained based on the first frame image, but it was closer to Ringelmann blackness level 2. Therefore, it was determined that the Ringelmann blackness level of exhaust gas of the vessel in this embodiment in the first frame image was 2.
S5. Based on the running time of the seagoing vessel, the multi frame images of which
indicate continuous occurrence of illegal seagoing vessel exhaust blackness, whether there
is seagoing vessel with illegal exhaust emission was determined. Specifically, the
Ringelmann blackness level and the driving time of the vessel to judge the emission
violation were determined by the rules formulated by the local law enforcement
department.
Claims (5)
1. A monitoring method for the exhaust emission of seagoing vessels by using smart
phones, characterized by the following steps.
Si. The law enforcement officers shoot the navigation video of the seagoing vessels
suspected of illegal emission through the smart phones, so as to obtain multiple images of
the suspected seagoing vessels. Wherein, in the video acquisition process, the smart phones
should always maintain the state as the first image acquisition.
S2. Based on the position information of the suspected seagoing vessel in the first image,
the remaining images obtained in S Iare processed in order to determine the position
information of the seagoing vessel in the remaining images. The specific treatment process
is as follows:
S201. A plane image coordinate system is established to describe the target area in the first
frame image by taking the row number of pixels in the image as ordinate x and the column
number as abscissa y. Specifically, the target area is a rectangular frame, divided into four
subregion: upper left, lower left, upper right and lower right. The centre point of the
rectangular frame is aligned with the chimney mouth of the target seagoing vessel, so that
the hull of the ship is located in one subregion, that is, hull subregion. Besides, the exhaust
gas is located in another subregion, that is, the exhaust subregion. Based on this, the
location information of the target area can be described as: [xi ~ xj, ym ~ yn], wherein, xi
and xj are the starting and ending row numbers of the rectangular region, j > i; ym and yn
are the starting and ending column numbers of the rectangular region, n > m. The total
pixel number of the target region z = (j-i+1) x (n-m+1).
S202. Using the edge operator of the hull subregion in the first frame image to highlight
the edge pixels in the region, and based on the position information of the hull subregion
in the first frame image- [xa ~ Xb, yc ~ yd], b > a, d > c, the images of nine regions to be
selected are intercepted in the second frame image with position information as [xa- ~ Xb
1, ye-1 ~ yd-1], [Xa-1 ~ Xb-1, ye ~ Yd], [Xa-1 ~ Xb-1, yc+1 ~ yd+1], [Xa ~ Xb, ye-1 ~ yd-1], [Xa~Xb, yc
yd], [Xa~Xb, yc+1~yd+1], [Xa+1~Xb+1, ye-l-yd-1], [Xa+1~Xb+1, ym-yd], [Xa+1~Xb+1, ym+1~
yd+1], respectively. Then the edge operator is used to process the images of nine regions to
highlight the edge pixels in the region.
S203. The luminance value of the hull subregion in the first frame image is calculated with
that of nine selected regions obtained from the second frame image, that is, the luminance
values of each pair of pixels with the same row and column number in each two regions
are subtracted and the absolute values are taken to obtain nine difference processing
images.
S204. The sum of all the pixel values in the nine difference processing images is obtained
respectively, and the difference processing image with the smallest sum result is selected.
The image of the selected region corresponding to the difference processing image is
determined as the hull subregion of the second frame image.
S205. Repeating the above S201 to S204, and the frame image is processed based on the
previous frame image of each frame image to obtain the hull subregion of the frame image,
thereby realizing the target seagoing vessel tracking.
S3. All the pixels in the target area of each image obtained in S are processed and sorted
in order of luminance value. By extracting the luminance values of the representative pixels, a Ringelmann colorimetric card for evaluating the blackness of exhaust gas in each image is made.
S4. Using the same image processing method as making Ringelmann colorimetric cards in
S3 to process and sort all the pixels of the exhaust subregion in the target area of each frame
image obtained in Si in order of luminance value. By extracting the luminance values of
the representative pixels and comparing with the Ringelmann colorimetric card based on
the same image, the blackness of the seagoing vessel exhaust gas in the image is judged.
S5. Based on the running time of the seagoing vessel, the multi frame images of which
indicate continuous occurrence of illegal seagoing vessel exhaust blackness, whether there
is seagoing vessel with illegal exhaust emission is determined.
2. According to claim 1, the monitoring method for the exhaust emission of seagoing
vessels by using smart phones, characterized in that in S3, the specific preparation method
of Ringelmann colorimetric card is as follows:
S301. The luminance values of all the pixels in the target area of the image are taken as the
processing objects, and the smallest luminance value among the blue luminance value
DNblue, green luminance value DNgreenand red luminance value DNred of each pixel is
selected as the grey value of the pixel.
S302. Taking the grey value as the abscissa and the pixel number as the ordinate, the grey
value histogram of all pixels is made. Further, in the grey value histogram, the grey value
of the M-th pixel ranking from small to large is defined as Ringelmann level 5 blackness,
and the grey value of the M-th pixel ranking from large to small is defined as Ringelmann
level 0 blackness.
S303. Based on the definition method of S302, correspondingly, the following is defined: the grey value of Ringelmann level 1 blackness = the grey value of Ringelmann level 0 blackness x 80% + the grey value of Ringelmann level 5 blackness x 20%; the grey value of Ringelmann level 2 blackness = the grey value of Ringelmann level 0 blackness x 60% + the grey value of Ringelmann level 5 blackness x 40%; the grey value of Ringelmann level 3 blackness = the grey value of Ringelmann level 0 blackness x 40% + the grey value of Ringelmann level 5 blackness x 60%; the grey value of Ringelmann level 4 blackness = the grey value of Ringelmann level 0 blackness x 20% + the grey value of Ringelmann level 5 blackness x 80%;
3. According to claim 2, the monitoring method for the exhaust emission of seagoing
vessels by using smart phones, characterized in that in S302, the value of M is taken as 1%
of the total pixel number in the target area of the image.
4. According to claim 1, the monitoring method for the exhaust emission of seagoing
vessels by using smart phones, characterized in that in S4, the specific implementation
method forjudging the blackness of seagoing vessel exhaust gas in the image is as follows:
S401. The luminance values of all the pixels in the target area of the image are taken as the
processing objects, and the smallest luminance value among the blue luminance value
DNblue, green luminance value DNgreenand red luminance value DNred of each pixel is
selected as the grey value of the pixel.
S402. Taking the grey value as the abscissa and the pixel number as the ordinate, the grey
value histogram of all pixels is made. Further, in the grey value histogram, the grey value
of the N-th pixel ranking from small to large is defined as grey value of exhaust gas.
S403. The grey value of the exhaust gas obtained in S402 is compared with the
Ringelmann colorimetric card based on the same image, and the Ringelmann blackness level closest to the grey value of the exhaust gas is taken as the Ringelmann blackness level of the seagoing vessel exhaust gas in the image.
5. According to claim 4, the monitoring method for the exhaust emission of seagoing
vessels by using smart phones, characterized in that in S402, the value of N is taken as1%
of the total pixel number in the target area of the image.
-1/2- 13 Jan 2021 2021100176
Figure 1
Figure 2
Figure 3
-2/2- 13 Jan 2021 2021100176
Figure 4
Figure 5
Figure 6
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