CN113962336B - Real-time cattle face ID coding method - Google Patents
Real-time cattle face ID coding method Download PDFInfo
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- CN113962336B CN113962336B CN202110899600.6A CN202110899600A CN113962336B CN 113962336 B CN113962336 B CN 113962336B CN 202110899600 A CN202110899600 A CN 202110899600A CN 113962336 B CN113962336 B CN 113962336B
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- 241000283690 Bos taurus Species 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 238000005286 illumination Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 44
- 238000001914 filtration Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000011176 pooling Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000012790 confirmation Methods 0.000 abstract description 2
- 238000003709 image segmentation Methods 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 241001465754 Metazoa Species 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 244000144972 livestock Species 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001454 recorded image Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device
- G06K17/0025—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device the arrangement consisting of a wireless interrogation device in combination with a device for optically marking the record carrier
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K11/00—Marking of animals
- A01K11/006—Automatic identification systems for animals, e.g. electronic devices, transponders for animals
Abstract
The invention discloses a real-time cattle face ID coding method, which comprises a data preprocessing stage and a cattle face recognition stage; the invention can utilize image recognition technology and any portable equipment to carry out non-invasive ID confirmation on the cattle; the method can confirm the body of the cattle only by collecting the front face of the cattle with the highest exposure probability, and has higher collection speed, stronger instantaneity and better production landing capability compared with the similar method; the invention adopts the real-time image segmentation and illumination self-adaption technology, greatly reduces the influence caused by background noise and illumination non-uniformity, and ensures that the system has better robustness in a non-standard environment.
Description
Technical Field
The invention relates to the technical field of image identification coding, in particular to a real-time cattle face ID coding method.
Background
With the development of animal husbandry, corresponding insurance claims are generated for the livestock which are correspondingly cultivated, when the livestock is in infringement, the whole animal is evaluated and then the claim is settled, but the claim has certain deviation, the individual calculation is more complex, meanwhile, when in infringement, the image obtained on site of the individual has obvious mismatching with the original recorded image due to various damages, meanwhile, the original robustness of the existing system is poor, and then the operability of the image is poor in the running process, so that the real-time cattle face ID coding method is proposed.
Disclosure of Invention
The invention aims to provide a real-time cow face ID coding method so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the real-time cattle face ID coding method comprises a data preprocessing stage and a cattle face recognition stage;
the data preprocessing stage comprises the following steps:
s101, preprocessing data;
s102, acquiring a frame of a camera;
s103, processing the cow frame images;
s104, acquiring a preprocessed image;
the face recognition stage comprises the following steps:
s201, correcting the posture of the cattle and amplifying the front face of the obtained preprocessed frame images;
s202, TTA processing and AI vector coding are carried out on the corrected and amplified frame images;
s203, performing Canopy noise data filtering processing on the image data obtained in the S202;
s204, performing cow self-adaptive weighted coding on the image data subjected to noise filtering.
According to the technical scheme, S103 is divided into three branches for processing:
firstly, carrying out cattle face positioning on an acquired frame, pre-matting an cattle image in the frame, and finally carrying out quality analysis and illumination analysis;
the second branch is used for firstly carrying out gesture recognition on the acquired frame, and then carrying out gesture filtering;
a third branch for dividing the acquired frame into images;
and (3) carrying out picture validity verification on the output signal of the first branch and the output signal of the second branch, and integrating the verification result and the output signal of the third branch to obtain an original value posture preprocessing picture, original posture data and original cow positioning data.
According to the above technical scheme, the AI vector coding in step S202 is divided into three parts, namely Target Dropout, depthwise Region Attention and Key Attention.
According to the above technical solution, the Target Dropout part divides the input image signal into two paths for processing, one path is to copy the feature map, and the other path is to average and pool the image signal, then perform channel attention processing, then perform saliency map screening, and sequentially perform NMS processing and Drop Block Mask Map processing, and then integrate the copied feature map for output.
According to the above technical solution, the Depthwise Region Attention part of the operation steps are to process the input image signal in three paths:
path one: subjecting the image signal to Featmap Depthwise 3 ×3Conv processing;
path two: carrying out channel guidance pooling on the image information;
and path III: performing GEMPooling processing on the image information;
and integrating the output data of the first path with the output data of the second path, performing characteristic splicing and sparse attention processing on the output data of the third path, performing BNNock processing on the processed data, and finally outputting the processed data.
According to the technical scheme, the Key attribute part comprises the following operation steps:
a) Carrying out Key Query processing;
b) Performing multi-head attention treatment;
c) Performing Layer Norm treatment;
d) Performing Lambda Attention treatment;
e) And outputting the processing result.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention can utilize image recognition technology and any portable equipment to carry out non-invasive ID confirmation on the cattle.
2. The method can confirm the body of the cattle only by collecting the front face of the cattle with the highest exposure probability, and has higher collection speed, stronger instantaneity and better production landing capability compared with the similar method.
3. The invention adopts the real-time image segmentation and illumination self-adaption technology, greatly reduces the influence caused by background noise and illumination non-uniformity, and ensures that the system has better robustness in a non-standard environment.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a schematic diagram of a neural network at the data preprocessing stage according to the present invention;
FIG. 3 is a block diagram of a neural network process flow in accordance with the present invention;
fig. 4 is a block diagram of AI vector coding flow in the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 1-4, the present invention provides a technical solution: the real-time cattle face ID coding method comprises a data preprocessing stage and a cattle face recognition stage;
the data preprocessing stage comprises the following steps:
s101, preprocessing data;
s102, acquiring a frame of a camera;
s103, processing the cow frame images;
s104, acquiring a preprocessed image;
the face recognition stage comprises the following steps:
s201, correcting the posture of the cattle and amplifying the front face of the obtained preprocessed frame images;
s202, TTA processing and AI vector coding are carried out on the corrected and amplified frame images;
s203, performing Canopy noise data filtering processing on the image data obtained in the S202;
s204, performing cow self-adaptive weighted coding on the image data subjected to noise filtering.
Specifically, S103 is divided into three branches for processing:
firstly, carrying out cattle face positioning on an acquired frame, pre-matting an cattle image in the frame, and finally carrying out quality analysis and illumination analysis;
the second branch is used for firstly carrying out gesture recognition on the acquired frame, and then carrying out gesture filtering;
a third branch for dividing the acquired frame into images;
and (3) carrying out picture validity verification on the output signal of the first branch and the output signal of the second branch, and integrating the verification result and the output signal of the third branch to obtain an original value posture preprocessing picture, original posture data and original cow positioning data.
Specifically, the AI vector code in step S202 is divided into three parts, target Dropout, depthwise Region Attention and Key Attention.
Specifically, the Target Dropout part includes the steps of dividing an input image signal into two paths for processing, wherein one path is a duplicate feature map, the other path is to firstly average pool the image signal, then perform channel attention processing, then perform saliency map screening, sequentially perform NMS processing and Drop Block Mask Map processing, and then integrate the duplicate feature map for output.
Specifically, the Depthwise Region Attention part operates to perform three-path processing on an input image signal:
path one: subjecting the image signal to Featmap Depthwise 3 ×3Conv processing;
path two: carrying out channel guidance pooling on the image information;
and path III: performing GEMPooling processing on the image information;
and integrating the output data of the first path with the output data of the second path, performing characteristic splicing and sparse attention processing on the output data of the third path, performing BNNock processing on the processed data, and finally outputting the processed data.
Specifically, the Key Attention part comprises the following operation steps:
a) Carrying out Key Query processing;
b) Performing multi-head attention treatment;
c) Performing Layer Norm treatment;
d) Performing Lambda Attention treatment;
e) And outputting the processing result.
In the description of the present invention, unless explicitly stated and limited otherwise, the terms "mounted," "connected," and "secured" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Standard parts used in the invention can be purchased from the market, special-shaped parts can be customized according to the description of the specification and the drawings, the specific connection modes of all parts adopt conventional means such as mature bolts, rivets, welding and the like in the prior art, machines, parts and equipment adopt conventional models in the prior art, and circuit connection adopts conventional connection modes in the prior art, so that the details are not described.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The real-time cow face ID coding method is characterized by comprising a data preprocessing stage and a cow face recognition stage;
the data preprocessing stage comprises the following steps:
s101, preprocessing data;
s102, acquiring a frame of a camera;
s103, processing the cow frame images, and dividing the cow frame images into three branches for processing:
firstly, carrying out cattle face positioning on an acquired frame, pre-matting an cattle image in the frame, and finally carrying out quality analysis and illumination analysis;
the second branch is used for firstly carrying out gesture recognition on the acquired frame, and then carrying out gesture filtering;
a third branch for dividing the acquired frame into images;
the output signal of the first branch and the output signal of the second branch are subjected to picture validity verification, and a verification result is integrated with the output signal of the third branch to obtain an original value posture preprocessing picture, original posture data and original cow positioning data;
s104, acquiring a preprocessed image;
the face recognition stage comprises the following steps:
s201, correcting the posture of the cattle and amplifying the front face of the obtained preprocessed frame images;
s202, TTA processing and AI vector coding are carried out on the corrected and amplified frame images;
s203, performing Canopy noise data filtering processing on the image data obtained in the S202;
s204, performing cow self-adaptive weighted coding on the image data subjected to noise filtering.
2. The real-time bovine front face ID encoding method according to claim 1, wherein: the AI vector coding in step S202 is divided into three parts, target Dropout, depthwise Region Attention and Key Attention.
3. The real-time bovine front face ID encoding method according to claim 2, wherein: the Target Dropout part is operated by dividing an input image signal into two paths for processing, wherein one path is a copy characteristic diagram, the other path is to firstly average pool the image signal, then carry out channel attention processing, then carry out saliency map screening, sequentially carry out NMS processing and Drop Block Mask Map processing, and then integrate the copy characteristic diagram output.
4. The real-time bovine front face ID encoding method according to claim 2, wherein: the Depthwise Region Attention part of the operation steps are to perform three paths of processing on the input image signal:
path one: subjecting the image signal to Featmap Depthwise 3 ×3Conv processing;
path two: carrying out channel guidance pooling on the image information;
and path III: performing GEMPooling processing on the image information;
and integrating the output data of the first path with the output data of the second path, performing characteristic splicing and sparse attention processing on the output data of the third path, performing BNNock processing on the processed data, and finally outputting the processed data.
5. The real-time bovine front face ID encoding method according to claim 2, wherein: the Key Attention part comprises the following operation steps:
a) Carrying out Key Query processing;
b) Performing multi-head attention treatment;
c) Performing Layer Norm treatment;
d) Performing Lambda Attention treatment;
e) And outputting the processing result.
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