CN111832422A - Night image recognition method and device for intelligent agricultural equipment and storage medium - Google Patents
Night image recognition method and device for intelligent agricultural equipment and storage medium Download PDFInfo
- Publication number
- CN111832422A CN111832422A CN202010564066.9A CN202010564066A CN111832422A CN 111832422 A CN111832422 A CN 111832422A CN 202010564066 A CN202010564066 A CN 202010564066A CN 111832422 A CN111832422 A CN 111832422A
- Authority
- CN
- China
- Prior art keywords
- image recognition
- recognition method
- image
- intelligent agricultural
- agricultural equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000003860 storage Methods 0.000 title claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000010586 diagram Methods 0.000 claims abstract description 6
- 238000005286 illumination Methods 0.000 claims description 13
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000003287 optical effect Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 4
- 238000001559 infrared map Methods 0.000 claims description 2
- 238000004590 computer program Methods 0.000 description 6
- 241000894007 species Species 0.000 description 5
- 241000196324 Embryophyta Species 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005507 spraying Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000009333 weeding Methods 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D34/00—Mowers; Mowing apparatus of harvesters
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Environmental Sciences (AREA)
- Multimedia (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Insects & Arthropods (AREA)
- Pest Control & Pesticides (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Artificial Intelligence (AREA)
- Image Analysis (AREA)
Abstract
The application discloses a night image identification method, a night image identification device and a storage medium for intelligent agricultural equipment, wherein an IR image is obtained by acquiring infrared images of a plurality of frames of crops and preprocessing the infrared images of the frames of crops; inputting the IR diagram into a pre-trained YOLO network to obtain the variety information of the crops; the mode through infrared image can realize the night and gather, and the kind information can be discerned fast from few and grey map to the YOLO network, has improved the accuracy of night image identification.
Description
Technical Field
The application relates to the technical field of image recognition, in particular to a night image recognition method and device for intelligent agricultural equipment and a storage medium.
Background
At present, intelligent agricultural instruments are widely applied to agricultural production, such as common weeding vehicles, flowering phase identification probes and agricultural spraying equipment, so that the labor intensity is greatly reduced, and the production efficiency is improved. The basis of realizing automation of the intelligent agricultural apparatus is an image recognition technology, the type of the crop is recognized through the image recognition technology, and then the operation corresponding to the type of the crop is executed. But some work needs to go on night, for example weed, pick fruit at night etc. and current scheme need keep light at night, and the camera of the intelligent agricultural apparatus of being convenient for gathers the image, and the electric energy that consumes is more, is unfavorable for cost control.
Disclosure of Invention
In order to overcome the defects of the prior art, the application aims to provide a night image recognition method, a night image recognition device and a storage medium for intelligent agricultural equipment, which can realize image recognition in a non-lighting environment.
The technical scheme adopted by the application for solving the problems is as follows: in a first aspect, the application provides a nighttime image recognition method for an intelligent agricultural apparatus, comprising the following steps:
acquiring a plurality of frames of infrared images of crops, and preprocessing the plurality of frames of infrared images to obtain an IR image;
and inputting the IR diagram into a previously trained YOLO network to obtain the variety information of the crops.
Further, the infrared image is collected by a depth camera.
Further, the depth camera is an optical depth camera in an Australia ratio.
Further, before acquiring the infrared images of the plurality of frames of crops, the method further comprises:
and acquiring the illumination intensity of the current environment, and acquiring the infrared image of the crop if the illumination intensity is less than a preset illumination threshold value.
Further, the obtaining of the crop type information specifically includes:
performing de-shading processing on the IR image;
acquiring a target crop gray scale map from the IR map;
and comparing the target crop gray-scale image with a pre-trained reference image to obtain the category information.
Further, after obtaining the crop species information, the method further includes:
and marking the target crop gray-scale map according to the category information, and inputting the marked target crop gray-scale map into the YOLO network for training.
Further, the preprocessing is used for converting a frame gray level image of a plurality of frames of the infrared images.
Further, the frame gray map conversion is completed based on opencv and python.
In a second aspect, the present application provides a nighttime image recognition device for an intelligent agricultural implement, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the nighttime image recognition method for a smart agricultural implement as described above.
In a third aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the nighttime image recognition method for an intelligent agricultural implement as described above.
In a fourth aspect, the present application also provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the nighttime image recognition method for a smart agricultural implement as described above.
One or more technical schemes provided in the embodiment of the application have at least the following beneficial effects: according to the method, a plurality of frames of infrared images of crops are obtained, and an IR image is obtained after the plurality of frames of infrared images are preprocessed; inputting the IR diagram into a pre-trained YOLO network to obtain the variety information of the crops; the mode through infrared image can realize the night and gather, and the kind information can be discerned fast from few and grey map to the YOLO network, has improved the accuracy of night image identification.
Drawings
The present application is further described below with reference to the following figures and examples.
Fig. 1 is a flowchart of a nighttime image recognition method for an intelligent agricultural implement according to an embodiment of the present application;
FIG. 2 is a flow chart of a nighttime image recognition method for a smart agricultural implement according to another embodiment of the present application;
FIG. 3 is a flow chart of a nighttime image recognition method for a smart agricultural implement according to another embodiment of the present application;
FIG. 4 is a flowchart of a nighttime image recognition method for an intelligent agricultural implement according to another embodiment of the present application;
fig. 5 is a schematic diagram of an apparatus for performing a nighttime image recognition method for an intelligent agricultural implement according to another embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that, if not conflicted, the various features of the embodiments of the present application may be combined with each other within the scope of protection of the present application. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.
Referring to fig. 1, a first embodiment of the present application provides a nighttime image recognition method for an intelligent agricultural implement, including the steps of:
s100, acquiring infrared images of a plurality of frames of crops, and preprocessing the infrared images to obtain an IR image;
and step S200, inputting the IR diagram into a previously trained YOLO network to obtain the crop species information.
It should be noted that the intelligent agricultural machine in this embodiment may be a common weeding vehicle, a chemical spraying device, or the like, and may be equipped with a camera to obtain an infrared image and a YOLO network to realize identification.
In one embodiment, because the content in the infrared image shot at night is less, the image recognition is preferably performed by adopting a YOLO network in the embodiment, compared with the traditional R-CNN or Fast R-CNN network, the calculation speed of the YOLO network is high, the recognition can be completed only by traversing the image once, and the method is more suitable for being used at night. It should be noted that the specific model and algorithm of the YOLO network may adopt any model in the prior art, and are not described herein again.
In an embodiment, one frame of image may be adopted in step S100, and some frames may be adopted, and this embodiment preferably includes at least two frames, because crops do not frequently move, the similarity between two adjacent frames of images is high, the IR image obtained by combining with the preprocessing includes more and clearer details, and the specific number of frames is selected according to actual needs.
In another embodiment of the present application, the infrared image is collected by a depth camera.
In an embodiment, the infrared image can be acquired through any type of camera, and the depth camera is preferably adopted in the embodiment, so that more space details can be acquired while the infrared image is acquired, and the crop identification is facilitated.
In another embodiment of the present application, the depth camera is an austempered medium depth camera.
In an embodiment, the depth camera can adopt any model in the prior art, and the optical depth camera in the optical contrast is preferably adopted in the embodiment, so that the intelligent degree is higher, the further development is easy, and the specific type of the depth camera can be selected according to actual requirements.
Referring to fig. 2, in another embodiment of the present application, before acquiring the infrared images of the crops, the method further includes:
step S110, obtaining the illumination intensity of the current environment, and obtaining the infrared image of the crop if the illumination intensity is smaller than a preset illumination threshold.
In an embodiment, because there is not no light at night, if the light incident to the outside is sufficient, the light can be identified through any image identification network, based on this, the present embodiment can obtain the illumination intensity through the light intensity sensor and other devices in the prior art, and set the illumination threshold in advance, and when the illumination intensity is less than the illumination threshold, the state of insufficient light is present.
Referring to fig. 3, in another embodiment of the present application, the deriving of the crop species information specifically includes:
step S210, carrying out shadow removing processing on the IR image;
step S220, acquiring a gray scale image of the target crop from the IR image;
and step S230, comparing the target crop gray-scale image with a pre-trained reference image to obtain species information.
In an embodiment, the steps S210 to S220 are preferable in this embodiment, and different steps may be adopted according to a specific YOLO network, so that the image recognition may be implemented.
It should be noted that the target plant can be segmented from the background by the shadow removal process, so that the background image is prevented from interfering with the identification of the target plant.
In an embodiment, the pre-trained reference picture can be obtained by manual labeling, and the specific labeling method is selected according to actual requirements.
Referring to fig. 4, in another embodiment of the present application, after obtaining the crop species information, the method further includes:
and S240, marking the target crop gray-scale map according to the category information, and inputting the marked target crop gray-scale map into a YOLO network for training.
In an embodiment, the recognized graph is labeled and then input into the YOLO network for training, so that the accuracy of the YOLO network can be further improved, and the next recognition is facilitated.
In another embodiment of the present application, the preprocessing includes frame grayscale map conversion of several frames of infrared images.
In one embodiment, the frame gray level image conversion is carried out on a plurality of frames of infrared images, the plurality of frames of infrared images can be combined into one IR image, the details are strengthened to a certain extent, and the accuracy of image identification is improved.
In another embodiment of the present application, frame grayscale map conversion is done based on opencv and python.
In an embodiment, OpenCV is a cross-platform computer vision and machine learning software library which is licensed based on BSD, can run on Linux, Windows, Android, and Mac OS operating systems, has the characteristics of light weight and high efficiency, provides interfaces of languages such as Python, Ruby, MATLAB, and the like, is more suitable for image processing, can also adopt other platforms for image processing according to actual needs, and is not described herein again.
Referring to fig. 5, another embodiment of the present application also provides a nighttime image recognition apparatus 5000 for an intelligent agricultural implement, including: memory 5100, control processor 5200, and a computer program stored on memory 5200 and executable on control processor 5100, the control processor when executing the computer program implementing the method for nighttime image recognition for smart agricultural implement as in any of the embodiments above, for example, performing method steps S100 to S200 in fig. 1, method step S110 in fig. 2, method steps S210 to S230 in fig. 3, and method step S240 in fig. 4 described above.
The control processor 5200 and the memory 5100 may be connected by a bus or other means, such as being connected by a bus in fig. 5.
The memory 5100, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory 5100 can include high-speed random access memory, and can also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 5100 optionally includes a memory remotely located from the control processor 5200, which may be connected to the nighttime image recognition device 5000 for smart agricultural implement via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Furthermore, another embodiment of the present application also provides a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors, for example, by one of the control processors 5200 in fig. 5, and may cause the one or more control processors 5200 to perform the nighttime image recognition method for an intelligent agricultural implement in the above-described method embodiment, for example, to perform the above-described method steps S100 to S200 in fig. 1, method step S110 in fig. 2, method steps S210 to S230 in fig. 3, and method step S240 in fig. 4.
It should be noted that, since the apparatus for executing the nighttime image recognition method for the intelligent agricultural equipment in the embodiment is based on the same inventive concept as the nighttime image recognition method for the intelligent agricultural equipment described above, the corresponding contents in the method embodiment are also applicable to the embodiment of the apparatus, and are not described in detail here.
Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (10)
1. A night image recognition method for intelligent agricultural equipment is characterized by comprising the following steps:
acquiring a plurality of frames of infrared images of crops, and preprocessing the plurality of frames of infrared images to obtain an IR image;
and inputting the IR diagram into a previously trained YOLO network to obtain the variety information of the crops.
2. The nighttime image recognition method for intelligent agricultural equipment according to claim 1, wherein: the infrared image is collected by a depth camera.
3. The nighttime image recognition method for intelligent agricultural equipment according to claim 2, wherein: the depth camera is an optical depth camera in an Australian contrast.
4. The nighttime image recognition method for intelligent agricultural equipment according to claim 1, wherein the acquiring of the infrared images of the plurality of frames of crops comprises:
and acquiring the illumination intensity of the current environment, and acquiring the infrared image of the crop if the illumination intensity is less than a preset illumination threshold value.
5. The night image recognition method for intelligent agricultural equipment according to claim 1, wherein the obtaining of the crop type information specifically comprises:
performing de-shading processing on the IR image;
acquiring a target crop gray scale map from the IR map;
and comparing the target crop gray-scale image with a pre-trained reference image to obtain the category information.
6. The night image recognition method for intelligent agricultural equipment according to claim 5, wherein after obtaining the crop species information, the method further comprises:
and marking the target crop gray-scale map according to the category information, and inputting the marked target crop gray-scale map into the YOLO network for training.
7. The nighttime image recognition method for intelligent agricultural equipment according to claim 1, wherein: the preprocessing comprises the step of converting a frame gray level image of a plurality of frames of the infrared image.
8. The nighttime image recognition method for intelligent agricultural equipment according to claim 7, wherein: the frame gray map conversion is completed based on opencv and python.
9. A nighttime image recognition device for smart agricultural equipment comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the nighttime image recognition method for a smart agricultural implement of any one of claims 1 to 8.
10. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the nighttime image recognition method for an intelligent agricultural implement according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010564066.9A CN111832422A (en) | 2020-06-18 | 2020-06-18 | Night image recognition method and device for intelligent agricultural equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010564066.9A CN111832422A (en) | 2020-06-18 | 2020-06-18 | Night image recognition method and device for intelligent agricultural equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111832422A true CN111832422A (en) | 2020-10-27 |
Family
ID=72897764
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010564066.9A Pending CN111832422A (en) | 2020-06-18 | 2020-06-18 | Night image recognition method and device for intelligent agricultural equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111832422A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115682510A (en) * | 2021-07-30 | 2023-02-03 | 海信集团控股股份有限公司 | Intelligent refrigerator, food material identification method, equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101226597A (en) * | 2007-01-18 | 2008-07-23 | 中国科学院自动化研究所 | Method and system for recognizing nights pedestrian based on thermal infrared gait |
| CN109508710A (en) * | 2018-10-23 | 2019-03-22 | 东华大学 | Based on the unmanned vehicle night-environment cognitive method for improving YOLOv3 network |
| CN109784336A (en) * | 2019-01-30 | 2019-05-21 | 云南电网有限责任公司电力科学研究院 | A kind of infrared image fault point recognition methods based on YOLO algorithm of target detection |
| WO2019138212A2 (en) * | 2018-01-10 | 2019-07-18 | Xihelm Limited | Method and system for agriculture |
| CN110619352A (en) * | 2019-08-22 | 2019-12-27 | 杭州电子科技大学 | Typical infrared target classification method based on deep convolutional neural network |
-
2020
- 2020-06-18 CN CN202010564066.9A patent/CN111832422A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101226597A (en) * | 2007-01-18 | 2008-07-23 | 中国科学院自动化研究所 | Method and system for recognizing nights pedestrian based on thermal infrared gait |
| WO2019138212A2 (en) * | 2018-01-10 | 2019-07-18 | Xihelm Limited | Method and system for agriculture |
| CN109508710A (en) * | 2018-10-23 | 2019-03-22 | 东华大学 | Based on the unmanned vehicle night-environment cognitive method for improving YOLOv3 network |
| CN109784336A (en) * | 2019-01-30 | 2019-05-21 | 云南电网有限责任公司电力科学研究院 | A kind of infrared image fault point recognition methods based on YOLO algorithm of target detection |
| CN110619352A (en) * | 2019-08-22 | 2019-12-27 | 杭州电子科技大学 | Typical infrared target classification method based on deep convolutional neural network |
Non-Patent Citations (1)
| Title |
|---|
| 傅隆生 等: "深度学习方法在农业信息中的研究进展与应用现状", 中国农业大学学报, no. 02, 15 February 2020 (2020-02-15), pages 111 - 126 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115682510A (en) * | 2021-07-30 | 2023-02-03 | 海信集团控股股份有限公司 | Intelligent refrigerator, food material identification method, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110059694B (en) | Intelligent identification method for character data in complex scene of power industry | |
| CN110532883B (en) | Improvement of on-line tracking algorithm by off-line tracking algorithm | |
| CN104077773A (en) | Image edge detection method, and image target identification method and device | |
| CN111860255B (en) | Training and using methods, devices, equipment and media of driving detection model | |
| CN110705531B (en) | Missing character detection and missing character detection model establishing method and device | |
| CN116665080B (en) | Unmanned aerial vehicle deteriorated insulator detection method and system based on target recognition | |
| CN109242000B (en) | Image processing method, device, equipment and computer readable storage medium | |
| CN113515655A (en) | Fault identification method and device based on image classification | |
| CN115512238A (en) | Method and device for determining damaged area, storage medium and electronic device | |
| Rahim et al. | Deep learning-based accurate grapevine inflorescence and flower quantification in unstructured vineyard images acquired using a mobile sensing platform | |
| CN112861755A (en) | Method and system for real-time segmentation of multiple classes of targets | |
| CN111832422A (en) | Night image recognition method and device for intelligent agricultural equipment and storage medium | |
| CN114227717A (en) | Intelligent inspection method, device, equipment and storage medium based on inspection robot | |
| CN114266963A (en) | Method and device for identifying winding gap of wire and storage medium | |
| CN113052295B (en) | Training method of neural network, object detection method, device and equipment | |
| CN107862314B (en) | Code spraying identification method and device | |
| CN110880003B (en) | Image matching method and device, storage medium and automobile | |
| WO2023207535A1 (en) | Pseudo label box generation method and apparatus, and electronic device | |
| CN112598013A (en) | Computer vision processing method based on neural network | |
| CN109785353B (en) | Secondary traversal binarization method and device for two-dimensional code and storage medium | |
| CN116311205A (en) | License plate recognition method, license plate recognition device, electronic equipment and storage medium | |
| CN115272284A (en) | Power transmission line defect identification method based on image quality evaluation | |
| CN114580573A (en) | Image-based cloud amount, cloud shape and weather phenomenon inversion device and method | |
| CN114758355A (en) | Underwater salmon weight estimation method and system based on machine vision | |
| Tian et al. | Real-time vehicle detection under complex road conditions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |