CN106776877B - Automatic identification method for motorcycle part models - Google Patents
Automatic identification method for motorcycle part models Download PDFInfo
- Publication number
- CN106776877B CN106776877B CN201611074703.4A CN201611074703A CN106776877B CN 106776877 B CN106776877 B CN 106776877B CN 201611074703 A CN201611074703 A CN 201611074703A CN 106776877 B CN106776877 B CN 106776877B
- Authority
- CN
- China
- Prior art keywords
- motorcycle
- parts
- shooting
- size
- shooting area
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/51—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Library & Information Science (AREA)
- Databases & Information Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention relates to a motorcycle part model automatic identification method, judge whether there is matched spare and accessory parts from the outline first, then judge the size of the spare and accessory parts of the motorcycle, can correspond to the concrete model of the spare and accessory parts of the motorcycle accurately, judge fast and accurately; if the output pictures are not accurate, the user can manually compare the output pictures so as to judge whether the output pictures are correct or not; the invention sets shooting areas with different sizes, judges the sizes of the motorcycle parts according to the sizes of the shooting areas and has small judgment precision error. In addition, motorcycle parts to be recognized can be roughly classified manually, the classification details of the rough classification are divided into nine major classes which are respectively related to engines and accessories, traveling system parts, operating system parts, electric appliances and instruments and general parts, and the motorcycle parts database is divided into corresponding nine major classes of databases, so that the comparison range can be reduced, the recognition speed is improved, and the recognition precision is relatively improved.
Description
Technical Field
The invention relates to an automatic identification method for motorcycle part models.
Background
Motorcycles are various in types, such as a knight vehicle, a camber beam vehicle, a scooter, an off-road vehicle, a beach vehicle, a farmer vehicle, a kart vehicle and the like, and each type of motorcycle involves a power part, a manipulation part, a vehicle body part, an electric part, a wearing part and the like which are different from one another. Each type of vehicle adopts spare and accessory parts with different types according to different manufacturers and different types produced by each manufacturer. Therefore, the motorcycle has a large variety of parts, which requires a high level of knowledge from the viewpoint of motorcycle maintenance personnel. Under the condition that a factory spare and accessory part list is not produced, the shapes of some spare and accessory parts are often almost the same, or some spare and accessory parts are not used very often, therefore, a motorcycle maintenance worker cannot accurately judge the models of the spare and accessory parts, and the motorcycle cannot be repaired.
In addition, the general spare and accessory parts need to be reserved, and for a motorcycle maintenance point, the reservation needs to be operated by a maintenance technician, which brings trouble to maintenance personnel and is not beneficial to the management of the maintenance point.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention provides an automatic motorcycle part model identification method, aiming at overcoming the defects that motorcycle parts are difficult to identify and the booking process is relatively troublesome.
The technical scheme adopted by the invention for solving the technical problems is as follows: a motorcycle part model automatic identification method comprises the following steps:
step 1: establishing a motorcycle part database, wherein the database comprises a motorcycle part photo and outline edge characteristics thereof as well as the corresponding model and size of the motorcycle part;
step 2: acquiring parts of a motorcycle to be identified;
and step 3: setting a plurality of shooting areas with different sizes, wherein each shooting area is fixed in size and is provided with a serial number, and the serial number is positioned at the edge part in the shooting area; selecting a shooting area with similar size according to the size of the motorcycle accessories, and placing the motorcycle accessories in the shooting area, wherein the motorcycle accessories are completely positioned in the shooting area;
and 4, step 4: shooting a picture of a shooting area with motorcycle accessories by using a shooting device, wherein the shooting device adjusts the shooting range by taking the position of the serial number as a reference point, so that the whole shooting area is in the shooting range, and the shot picture is provided with the serial number of the shooting area;
and 5: converting the picture in the step 4 into a gray image, and setting the gray image as an original gray image;
step 6: acquiring the contour edge characteristics of the original gray level image in the step 5 through an edge processing algorithm;
and 7: matching feature points of the contour edge features in the step 6, acquiring the number of a shooting area shot by the shooting reference point part, and acquiring the size of the shooting area according to the number; then, contour edge characteristics of other parts are obtained, the size of the outer contour of the shot motorcycle part is correspondingly obtained, and the contour edge characteristics are compared with the contour of the motorcycle part in the database in the step 1, so that the contour edge characteristics of the parts in the database and the parts to be identified are in a minimum deviation state;
and 8: judging whether the profile tolerance error is within a preset profile tolerance error range, if so, executing a step 9; if not, executing the step 10;
and step 9: comparing the size of the outer contour of the motorcycle part to be identified obtained in the step 7 with the size of the motorcycle part in the database, judging whether the size error is within a preset size error range, and if so, executing a step 11; if not, executing the step 10;
step 10: outputting the parts of the motorcycle which are not found correspondingly;
step 11: and extracting the photos and the models of the corresponding motorcycle parts in the database and outputting the photos and the models.
The motorcycle parts are waste parts detached from the motorcycle.
The edge of the shooting area is provided with an edge line, and the shooting device adjusts the shooting range by identifying the edge line.
The shooting area is rectangular, the edge line is a rectangular frame, the serial number of the shooting area is positioned at the upper left corner of the rectangular frame, the serial number is positioned in a circle, and the shooting reference point of the shooting device is the circle center of the circle.
Before step 1, roughly classifying motorcycle parts to be identified manually, wherein the classification details of the rough classification are nine major classes, namely engine and parts, walking system parts, control system parts, electric appliances and instruments, and general parts and related parts, and the motorcycle parts database is divided into corresponding nine major classes. Therefore, the parts can be roughly classified, the comparison range is reduced, the identification speed is improved, and the identification precision is relatively improved.
The motorcycle part model automatic identification method has the advantages that whether matched parts exist or not is judged from the outer contour, then the size of the motorcycle parts is judged, the motorcycle part model automatic identification method can accurately correspond to the specific models of the motorcycle parts, and the judgment is fast and accurate; if the output pictures are not accurate, the user can manually compare the output pictures so as to judge whether the output pictures are correct or not; the invention sets shooting areas with different sizes, judges the sizes of the motorcycle parts according to the sizes of the shooting areas and has small judgment precision error.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart of the motorcycle part model automatic identification method of the present invention.
Fig. 2 is a schematic diagram of one photographing region in the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
As shown in figure 1, the motorcycle part model automatic identification method of the invention comprises the following steps:
step 1: establishing a motorcycle part database, wherein the database comprises a motorcycle part photo and outline edge characteristics thereof as well as the corresponding model and size of the motorcycle part;
step 2: acquiring parts of a motorcycle to be identified;
and step 3: setting a plurality of shooting areas with different sizes, wherein each shooting area is fixed in size and is provided with a serial number, and the serial number is positioned at the edge part in the shooting area; selecting a shooting area with similar size according to the size of the motorcycle accessories, and placing the motorcycle accessories in the shooting area, wherein the motorcycle accessories are completely positioned in the shooting area;
and 4, step 4: shooting a picture of a shooting area with motorcycle accessories by using a shooting device, wherein the shooting device adjusts the shooting range by taking the position of the serial number as a reference point, so that the whole shooting area is in the shooting range, and the shot picture is provided with the serial number of the shooting area;
and 5: converting the picture in the step 4 into a gray image, and setting the gray image as an original gray image;
step 6: acquiring the contour edge characteristics of the original gray level image in the step 5 through an edge processing algorithm;
and 7: matching feature points of the contour edge features in the step 6, acquiring the number of a shooting area shot by the shooting reference point part, and acquiring the size of the shooting area according to the number; then, contour edge characteristics of other parts are obtained, the size of the outer contour of the shot motorcycle part is correspondingly obtained, and the contour edge characteristics are compared with the contour of the motorcycle part in the database in the step 1, so that the contour edge characteristics of the parts in the database and the parts to be identified are in a minimum deviation state;
the method specifically comprises the following substeps:
step S71: extracting a partial point set A of the edge contour features of the accessories of the database and a partial point set B of the edge contour features of the accessories to be identified;
step S72: and comparing the edge profile characteristic points of the database accessories and the accessories to be identified one by one to obtain the minimum Hausdorff distance measurement between the point set A and the point set B, wherein the point set A and the point set B have the highest matching similarity, and the profile edge characteristics of the database accessories and the accessories to be identified are in the minimum deviation state.
And 8: judging whether the profile tolerance error is within a preset profile tolerance error range, if so, executing a step 9; if not, executing the step 10;
the method specifically comprises the following substeps:
step S81: respectively acquiring all characteristic point coordinates of the contour lines of the database accessories and the accessories to be identified;
step S82: selecting a characteristic point ak (x1, y1) of the contour line of the part to be identified, and selecting two discrete points a (k-n) and a (k + n) which are adjacent to each other at the same interval at the left and right near the corresponding ak point;
step S83: connecting the origin O with the point a (k-n) to obtain a straight line, connecting the origin O with the point a (k + n) to obtain another straight line, and intersecting the two straight lines with the contour line of the standard part at two points B1 and B2 respectively to obtain a point set B between the points B1 and B2; then, in the point set B on the contour line of the database part, a theoretical characteristic point corresponding to the point ak to be measured is inevitably found.
Step S84: and comparing the distance between two points between the point ak to be measured and the point set B by adopting a golden segmentation method, finding two points which are the shortest from the point ak to be measured on the point set B, then obtaining a straight line L, and solving the distance from the point ak to be measured to the straight line L, namely the minimum distance from the point ak to be measured to the standard contour, namely the contour error.
And step 9: comparing the size of the outer contour of the motorcycle part to be identified obtained in the step 7 with the size of the motorcycle part in the database, judging whether the size error is within a preset size error range, and if so, executing a step 11; if not, executing the step 10;
step 10: outputting the parts of the motorcycle which are not found correspondingly;
step 11: and extracting the photos and the models of the corresponding motorcycle parts in the database and outputting the photos and the models.
The motorcycle parts are waste parts detached from the motorcycle.
The edge of the shooting area is provided with an edge line, and the shooting device adjusts the shooting range by identifying the edge line.
The shooting area is rectangular, the edge line is a rectangular frame, the serial number of the shooting area is positioned at the upper left corner of the rectangular frame, the serial number is positioned in a circle, and the shooting reference point of the shooting device is the circle center of the circle.
Before step 1, roughly classifying motorcycle parts to be identified manually, wherein the classification details of the rough classification are nine major classes, namely engine and parts, walking system parts, control system parts, electric appliances and instruments, and general parts and related parts, and the motorcycle parts database is divided into corresponding nine major classes. Therefore, the parts can be roughly classified, the comparison range is reduced, the identification speed is improved, and the identification precision is relatively improved.
The motorcycle accessory classification list includes:
engine and accessories: the device comprises an assembly, a crankcase, a cylinder, a piston, a ring, a crankshaft, an air valve, a camshaft, a fuel tank, a filter, a fuel pump, an oil pump, a carburetor, electronic injection, air intake and exhaust, a water tank fan and the like;
the transmission system comprises parts of a motorcycle clutch, a transmission operating device, a motorcycle starting mechanism, a motorcycle belt transmission assembly, a chain transmission assembly and a shaft transmission assembly;
traveling system parts: frame, fender, fork, shock absorber fittings, wheels, hubs, rims, etc., tires, lock alarms, bumpers, sight glasses, glove boxes, bezels, etc., parking frames, armrests, windshields, accessories;
operating system parts: the steering column, the handlebar sleeve and the control device, the flexible shaft, the zipper, the brake pedal, the pull rod, the ABS and other brake parts;
electrical appliances and meters: storage batteries, generators, starter motors, brushes, etc.; rectifier, overrunning clutch, distributor, spark plug, switch, lamp and signal device, horn, meter, sensor, relay, wire bundle, counter, other electrical parts;
general parts and related: sealing element rubber, plastic parts, hard pipes, hoses, powder metallurgy parts, casting and forging parts, stamping parts, standard parts, fasteners, bearings, shaft sleeves, gears, coatings, adhesives, lubricating oil, other materials and processing, magneto motorcycle safety articles, helmets and sunglasses.
The motorcycle part model automatic identification method of the invention judges whether matched parts exist or not from the outer contour, then judges the size of the motorcycle parts, can accurately correspond to the specific model of the motorcycle parts, and can judge quickly and accurately; if the output pictures are not accurate, the user can manually compare the output pictures so as to judge whether the output pictures are correct or not; the invention sets shooting areas with different sizes, judges the sizes of the motorcycle parts according to the sizes of the shooting areas and has small judgment precision error.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (5)
1. A motorcycle part model automatic identification method is characterized by comprising the following steps:
step 1: establishing a motorcycle part database, wherein the database comprises a motorcycle part photo and outline edge characteristics thereof as well as the corresponding model and size of the motorcycle part;
step 2: acquiring parts of a motorcycle to be identified;
and step 3: setting a plurality of shooting areas with different sizes, wherein each shooting area is fixed in size and is provided with a serial number, and the serial number is positioned at the edge part in the shooting area; selecting a shooting area with similar size according to the size of the motorcycle accessories, and placing the motorcycle accessories in the shooting area, wherein the motorcycle accessories are completely positioned in the shooting area;
and 4, step 4: shooting a picture of a shooting area with motorcycle accessories by using a shooting device, wherein the shooting device adjusts the shooting range by taking the position of the serial number as a reference point, so that the whole shooting area is in the shooting range, and the shot picture is provided with the serial number of the shooting area;
and 5: converting the picture in the step 4 into a gray image, and setting the gray image as an original gray image;
step 6: acquiring the contour edge characteristics of the original gray level image in the step 5 through an edge processing algorithm;
and 7: matching feature points of the contour edge features in the step 6, acquiring the number of a shooting area shot by the shooting reference point part, and acquiring the size of the shooting area according to the number; then, contour edge characteristics of other parts are obtained, the size of the outer contour of the shot motorcycle part is correspondingly obtained, and the contour edge characteristics are compared with the contour of the motorcycle part in the database in the step 1, so that the contour edge characteristics of the parts in the database and the parts to be identified are in a minimum deviation state;
and 8: judging whether the profile tolerance error is within a preset profile tolerance error range, if so, executing a step 9; if not, executing the step 10;
and step 9: comparing the size of the outer contour of the motorcycle part to be identified obtained in the step 7 with the size of the motorcycle part in the database, judging whether the size error is within a preset size error range, and if so, executing a step 11; if not, executing the step 10;
step 10: outputting the parts of the motorcycle which are not found correspondingly;
step 11: and extracting the photos and the models of the corresponding motorcycle parts in the database and outputting the photos and the models.
2. A method of automatically identifying the type of motorcycle parts as claimed in claim 1, wherein: the motorcycle parts are waste parts detached from the motorcycle.
3. A method of automatically identifying the type of motorcycle parts as claimed in claim 1, wherein: the edge of the shooting area is provided with an edge line, and the shooting device adjusts the shooting range by identifying the edge line.
4. A motorcycle part model automatic identification method as claimed in claim 3, characterized in that: the shooting area is rectangular, the edge line is a rectangular frame, the serial number of the shooting area is positioned at the upper left corner of the rectangular frame, the serial number is positioned in a circle, and the shooting reference point of the shooting device is the circle center of the circle.
5. A method of automatically identifying the type of motorcycle parts as claimed in claim 1, wherein: before step 1, roughly classifying motorcycle parts to be identified manually, wherein the classification details of the rough classification are nine major classes, namely engine and parts, walking system parts, control system parts, electric appliances and instruments, and general parts and related parts, and the motorcycle parts database is divided into corresponding nine major classes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611074703.4A CN106776877B (en) | 2016-11-28 | 2016-11-28 | Automatic identification method for motorcycle part models |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611074703.4A CN106776877B (en) | 2016-11-28 | 2016-11-28 | Automatic identification method for motorcycle part models |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106776877A CN106776877A (en) | 2017-05-31 |
| CN106776877B true CN106776877B (en) | 2020-05-01 |
Family
ID=58900650
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611074703.4A Active CN106776877B (en) | 2016-11-28 | 2016-11-28 | Automatic identification method for motorcycle part models |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106776877B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108508848B (en) * | 2018-04-20 | 2019-12-06 | 华中科技大学 | Interpolation data-based milling contour error evaluation method |
| CN108764938A (en) * | 2018-05-07 | 2018-11-06 | 上海博泰悦臻电子设备制造有限公司 | A kind of non-certified products recognition methods of auto repair part and system |
| CN111103014A (en) * | 2019-12-27 | 2020-05-05 | 株洲壹星科技股份有限公司 | Automatic identification method and device for types of relay and contactor |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100459479B1 (en) * | 2002-07-09 | 2004-12-03 | 엘지산전 주식회사 | Grouping apparatus for automobile and method thereof |
| CN101770145A (en) * | 2010-01-12 | 2010-07-07 | 圆展科技股份有限公司 | Method for estimating actual size of object and real object projector |
| CN105158268A (en) * | 2015-09-21 | 2015-12-16 | 武汉理工大学 | Intelligent online detection method, system and device for defects of fine-blanked parts |
| CN105282461A (en) * | 2014-06-19 | 2016-01-27 | 斯克林集团公司 | Image processing apparatus image, acquiring apparatus, image processing method and image acquiring method |
| CN105509653A (en) * | 2015-11-30 | 2016-04-20 | 广州超音速自动化科技股份有限公司 | Method and system for visual measurement of profile of machine part |
| CN106096563A (en) * | 2016-06-17 | 2016-11-09 | 深圳市易特科信息技术有限公司 | Plant automatic recognition system and method |
| CN106153639A (en) * | 2015-04-21 | 2016-11-23 | 凯吉凯精密电子技术开发（苏州）有限公司 | circuit board detecting method based on artificial intelligence and detection device thereof |
-
2016
- 2016-11-28 CN CN201611074703.4A patent/CN106776877B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100459479B1 (en) * | 2002-07-09 | 2004-12-03 | 엘지산전 주식회사 | Grouping apparatus for automobile and method thereof |
| CN101770145A (en) * | 2010-01-12 | 2010-07-07 | 圆展科技股份有限公司 | Method for estimating actual size of object and real object projector |
| CN105282461A (en) * | 2014-06-19 | 2016-01-27 | 斯克林集团公司 | Image processing apparatus image, acquiring apparatus, image processing method and image acquiring method |
| CN106153639A (en) * | 2015-04-21 | 2016-11-23 | 凯吉凯精密电子技术开发（苏州）有限公司 | circuit board detecting method based on artificial intelligence and detection device thereof |
| CN105158268A (en) * | 2015-09-21 | 2015-12-16 | 武汉理工大学 | Intelligent online detection method, system and device for defects of fine-blanked parts |
| CN105509653A (en) * | 2015-11-30 | 2016-04-20 | 广州超音速自动化科技股份有限公司 | Method and system for visual measurement of profile of machine part |
| CN106096563A (en) * | 2016-06-17 | 2016-11-09 | 深圳市易特科信息技术有限公司 | Plant automatic recognition system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106776877A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106776877B (en) | Automatic identification method for motorcycle part models | |
| JP5299513B2 (en) | Obstacle detection device and method, and obstacle detection system | |
| CN105814415B (en) | Method for estimating vehicle mass | |
| TW201843611A (en) | Part information query method and system, electronic device and computer storage medium | |
| CN108647791B (en) | Multi-source automobile safety information processing method, device and system | |
| CN105528789B (en) | Robot visual orientation method and device, vision calibration method and device | |
| EP3159828B1 (en) | Adaptive calibration using visible car details | |
| CN103150904A (en) | Bayonet vehicle image identification method based on image features | |
| US11017247B2 (en) | Traffic signal recognition system | |
| CN107016362B (en) | Vehicle weight recognition method and system based on vehicle front windshield pasted mark | |
| CN107478289B (en) | The method and apparatus for obtaining average fuel consumption | |
| CN114595847A (en) | Method and system for providing vehicle repair tips | |
| CN105023452B (en) | A kind of method and device of multichannel traffic lights signal acquisition | |
| CN106945050B (en) | Intelligent maintenance method, robot and system for vehicle | |
| CN107851325A (en) | The detection method that falls down to the ground of shared bicycle | |
| KR102116725B1 (en) | Apparatus and method for distinguishing position of inlet based on recognition of license plate in stopped state | |
| CN111523352A (en) | Method for intelligently and rapidly identifying illegal modified vehicle and monitoring system thereof | |
| CN109934362A (en) | A kind of method, apparatus and terminal device of vehicle detection | |
| CN104346833A (en) | Vehicle restructing algorithm based on monocular vision | |
| JPWO2018179340A1 (en) | Inter-tooth error removal method and apparatus for engine vehicle and crank pulser rotor | |
| US20180017006A1 (en) | Method of detecting engine tuning of vehicle | |
| CN110529306A (en) | A kind of real-time monitoring device and its monitoring method of automobile filter service life | |
| CN115055964B (en) | Intelligent assembling method and system based on fuel injection pump | |
| EP3410705B1 (en) | 3d vision system for a motor vehicle and method of controlling a 3d vision system | |
| CN106980859A (en) | A kind of method for being used to recognize that aluminum vehicle wheel carries on the back chamber character |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |