CN114542902A - Image classification system based on deep learning - Google Patents

Image classification system based on deep learning Download PDF

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Publication number
CN114542902A
CN114542902A CN202210181176.6A CN202210181176A CN114542902A CN 114542902 A CN114542902 A CN 114542902A CN 202210181176 A CN202210181176 A CN 202210181176A CN 114542902 A CN114542902 A CN 114542902A
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China
Prior art keywords
module
deep learning
motor
classification system
image classification
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Withdrawn
Application number
CN202210181176.6A
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Chinese (zh)
Inventor
董震江
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Hebi College of Vocation and Technology
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Hebi College of Vocation and Technology
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Priority to CN202210181176.6A priority Critical patent/CN114542902A/en
Publication of CN114542902A publication Critical patent/CN114542902A/en
Withdrawn legal-status Critical Current

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16M—FRAMES, CASINGS OR BEDS OF ENGINES, MACHINES OR APPARATUS, NOT SPECIFIC TO ENGINES, MACHINES OR APPARATUS PROVIDED FOR ELSEWHERE; STANDS; SUPPORTS
    • F16M11/00—Stands or trestles as supports for apparatus or articles placed thereon ; Stands for scientific apparatus such as gravitational force meters
    • F16M11/02—Heads
    • F16M11/04—Means for attachment of apparatus; Means allowing adjustment of the apparatus relatively to the stand
    • F16M11/06—Means for attachment of apparatus; Means allowing adjustment of the apparatus relatively to the stand allowing pivoting
    • F16M11/12—Means for attachment of apparatus; Means allowing adjustment of the apparatus relatively to the stand allowing pivoting in more than one direction
    • F16M11/121—Means for attachment of apparatus; Means allowing adjustment of the apparatus relatively to the stand allowing pivoting in more than one direction constituted of several dependent joints
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16M—FRAMES, CASINGS OR BEDS OF ENGINES, MACHINES OR APPARATUS, NOT SPECIFIC TO ENGINES, MACHINES OR APPARATUS PROVIDED FOR ELSEWHERE; STANDS; SUPPORTS
    • F16M11/00—Stands or trestles as supports for apparatus or articles placed thereon ; Stands for scientific apparatus such as gravitational force meters
    • F16M11/02—Heads
    • F16M11/18—Heads with mechanism for moving the apparatus relatively to the stand
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16M—FRAMES, CASINGS OR BEDS OF ENGINES, MACHINES OR APPARATUS, NOT SPECIFIC TO ENGINES, MACHINES OR APPARATUS PROVIDED FOR ELSEWHERE; STANDS; SUPPORTS
    • F16M11/00—Stands or trestles as supports for apparatus or articles placed thereon ; Stands for scientific apparatus such as gravitational force meters
    • F16M11/42—Stands or trestles as supports for apparatus or articles placed thereon ; Stands for scientific apparatus such as gravitational force meters with arrangement for propelling the support stands on wheels
    • F16M11/425—Stands or trestles as supports for apparatus or articles placed thereon ; Stands for scientific apparatus such as gravitational force meters with arrangement for propelling the support stands on wheels along guiding means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Studio Devices (AREA)

Abstract

The invention belongs to the technical field of image processing, and particularly relates to an image classification system based on deep learning, which comprises a cross beam, wherein the bottom of the cross beam is provided with a guide rail, the bottom of the guide rail is provided with a rail motor, and the bottom of the rail motor is provided with an electric turntable; the fly leaf, the fly leaf sets up the bottom at electric turntable, the bottom both sides of fly leaf are provided with bent type pole, one side of bent type pole is provided with servo motor, servo motor's output runs through bent type pole and is provided with the axis of rotation, be provided with the mounting bracket in the axis of rotation, one side of mounting bracket is provided with the camera, and its is rational in infrastructure, and through the cooperation of track motor and guide rail and crossbeam, the regulation of camera position has been realized, and convenient wider image acquisition through the cooperation of being electric turntable and fly leaf, has realized the angular rotation regulation, through servo motor, axis of rotation, mounting bracket and camera's cooperation, has made things convenient for vertical angle's regulation.

Description

Image classification system based on deep learning
Technical Field
The invention relates to the technical field of image processing, in particular to an image classification system based on deep learning.
Background
With the expansion of the internet range, the perfection of related applications and the continuous development of intelligent hardware performance, image, text, audio and video data are continuously and explosively increased, the image is used as a carrier of visual information and is used as a basis of application fields such as image processing, pattern recognition, machine learning and artificial intelligence, and the process comprises the steps of image preprocessing, image feature extraction, feature dimension reduction and feature selection, classifier design and the like.
The existing image classification system based on deep learning has some defects in the using process, such as the fact that the angle of camera collection is not convenient to adjust, the position is fixed, the camera collection is not convenient to adjust, the limitation is large, and therefore a novel image classification system based on deep learning is provided for solving the problems.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The invention is provided in view of the problems in the existing image classification system based on deep learning.
Therefore, the invention aims to provide an image classification system based on deep learning, which can conveniently adjust the acquisition angle of a camera in the using process and improve the acquisition efficiency.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
an image classification system based on deep learning, comprising:
the device comprises a cross beam, a guide rail, a track motor and an electric turntable, wherein the bottom of the cross beam is provided with the guide rail;
the movable plate is arranged at the bottom of the electric turntable, curved rods are arranged on two sides of the bottom of the movable plate, a servo motor is arranged on one side of each curved rod, the output end of the servo motor penetrates through the curved rods to be provided with a rotating shaft, a mounting frame is arranged on the rotating shaft, a camera is arranged on one side of the mounting frame, a driving motor is arranged on one side of each curved rod, and the output end of the driving motor penetrates through the curved rods to be provided with a dust removal roller brush;
the control box, the control box sets up the front surface at track motor, the inner chamber of control box is provided with treater, receiving module, image processing module, degree of depth study classification module, storage module and drive module, treater electrical property input connection camera and receiving module, the handheld terminal of receiving module electrical property input connection, treater both way junction image processing module and degree of depth study classification module, treater electrical property input connection storage module and drive module, drive module electrical output connects track motor, driving motor, servo motor and electric turntable.
As a preferable aspect of the deep learning based image classification system according to the present invention, wherein: and mounting screw holes and mounting bolts matched with the mounting screw holes are arranged on two sides of the cross beam.
As a preferable aspect of the deep learning based image classification system according to the present invention, wherein: and light supplementing lamps are arranged on two sides of the mounting rack.
As a preferable aspect of the deep learning based image classification system according to the present invention, wherein: the handheld terminal is one of a smart phone, a tablet computer, a notebook computer or a PC terminal.
As a preferable aspect of the deep learning based image classification system according to the present invention, wherein: the inner cavity of the control box is provided with an independent power supply, and one side of the control box is provided with a power supply interface.
As a preferable aspect of the deep learning based image classification system according to the present invention, wherein: the inner cavity of the control box is also provided with a wireless transmission module, and the wireless transmission module is connected with an external display device.
Compared with the prior art, the invention has the beneficial effects that: through the cooperation of track motor and guide rail and crossbeam, realized the regulation of camera position, convenient wider image acquisition, through the cooperation of electric turntable and fly leaf, realized the angular rotation regulation, through the cooperation of servo motor, the axis of rotation, mounting bracket and camera, made things convenient for vertical angle's regulation, through handheld terminal, receiving module treater and drive module's cooperation, realized remote control, convenient operation, efficiency is higher.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail with reference to the accompanying drawings and detailed embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise. Wherein:
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic view of the dust removing roller brush of the present invention;
FIG. 3 is a schematic diagram of the system framework of the present invention.
In the figure; 100 cross beams, 110 guide rails, 120 track motors, 130 electric rotating discs, 200 movable plates, 210 curved rods, 220 servo motors, 230 rotating shafts, 240 mounting frames, 250 cameras, 260 light supplementing lamps, 270 driving motors, 280 dust removing roller brushes, 300 control boxes, 310 processors, 320 receiving modules, 330 handheld terminals, 340 image processing modules, 350 deep learning classification modules, 360 storage modules and 370 driving modules.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein for convenience of illustration, the cross-sectional view of the device structure is not enlarged partially according to the general scale, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides the following technical scheme: an image classification system based on deep learning is provided, which facilitates adjusting the collection angle of a camera and improves the collection efficiency during the use process, and please refer to fig. 1 to 3, and comprises a beam 100, a movable plate 200 and a control box 300;
referring to fig. 1 to 3 again, the bottom of the cross beam 100 is provided with a guide rail 110, the bottom of the guide rail 110 is provided with a rail motor 120, and the bottom of the rail motor 120 is provided with an electric turntable 130, specifically, the bottom of the cross beam 100 is welded with the guide rail 110, the bottom of the guide rail 110 is connected with the rail motor 120 in a sliding manner, and the bottom of the rail motor 120 is connected with the electric turntable 130 in a threaded manner;
referring to fig. 1 to 3 again, the movable plate 200 is disposed at the bottom of the electric turntable 130, the curved rods 210 are disposed at two sides of the bottom of the movable plate 200, the servo motor 220 is disposed at one side of the curved rods 210, the output end of the servo motor 220 penetrates through the curved rods 210 and is provided with the rotating shaft 230, the mounting frame 240 is disposed on the rotating shaft 230, the camera 250 is disposed at one side of the mounting frame 240, the driving motor 270 is disposed at one side of the curved rods 210, the dust removing roller brush 280 is disposed at the output end of the driving motor 270 and penetrates through the curved rods 210, specifically, the movable plate 200 is screwed to the bottom of the electric turntable 130, the curved rods 210 are welded at two sides of the bottom of the movable plate 200, the servo motor 220 is screwed to one side of the curved rods 210, the rotating shaft 230 is screwed to the output end of the servo motor 220, the mounting frame 240 is sleeved on the rotating shaft 230, the camera 250 is screwed to one side of the mounting frame 240, the driving motor 270 is screwed to one side of the curved rods 210, the output end of the driving motor 270 penetrates through the curved rod 210 and is screwed with a dust removing roller brush 280;
referring to fig. 1 to 3 again, the control box 300 is disposed on the front surface of the track motor 120, the inner cavity of the control box 300 is provided with a processor 310, a receiving module 320, an image processing module 340, a deep learning classification module 350, a storage module 360 and a driving module 370, the processor 310 is electrically connected to the camera 250 and the receiving module 320, the receiving module 320 is electrically connected to the handheld terminal 330, the processor 310 is connected to the image processing module 340 and the deep learning classification module 350 in a bidirectional manner, the processor 310 is electrically connected to the storage module 360 and the driving module 370 in an input manner, the driving module 370 is electrically connected to the track motor 120, the driving motor 270, the servo motor 220 and the electric turntable 130 in an output manner, specifically, the control box 300 is screwed on the front surface of the track motor 120, and the processor 310, the receiving module 320, the image processing module 340, the deep learning classification module 350, the electric turntable 130, the inner cavity of the control box 300 is adhered to the receiving module 320, the image processing module 340, the deep learning classification module 350, the electric turntable 130, and the electric turntable 130, The system comprises a storage module 360 and a driving module 370, a processor 310 is electrically connected with a camera 250 and a receiving module 320 in an input mode, the receiving module 320 is electrically connected with a handheld terminal 330 in an input mode, the processor 310 is connected with an image processing module 340 and a deep learning classification module 350 in a bidirectional mode, the processor 310 is electrically connected with the storage module 360 and the driving module 370 in an input mode, and the driving module 370 is electrically connected with a track motor 120, a driving motor 270, a servo motor 220 and an electric turntable 130 in an output mode.
The working principle is as follows: in the using process of the invention, the position of the camera 250 is adjusted through the matching of the track motor 120, the guide rail 110 and the beam 100, so that the image acquisition in a wider range is facilitated, the angle rotation adjustment is achieved through the matching of the electric turntable 130 and the movable plate 200, the vertical angle adjustment is facilitated through the matching of the servo motor 220, the rotating shaft 230, the mounting frame 240 and the camera 250, the remote control is achieved through the matching of the handheld terminal 330, the receiving module 320, the processor 310 and the driving module 370, so that the operation is facilitated, and the efficiency is higher.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (6)

1. An image classification system based on deep learning, comprising:
the device comprises a cross beam (100), wherein a guide rail (110) is arranged at the bottom of the cross beam (100), a track motor (120) is arranged at the bottom of the guide rail (110), and an electric turntable (130) is arranged at the bottom of the track motor (120);
the dust removal device comprises a movable plate (200), wherein the movable plate (200) is arranged at the bottom of an electric rotating disc (130), bent rods (210) are arranged on two sides of the bottom of the movable plate (200), a servo motor (220) is arranged on one side of each bent rod (210), the output end of the servo motor (220) penetrates through each bent rod (210) to be provided with a rotating shaft (230), a mounting frame (240) is arranged on each rotating shaft (230), a camera (250) is arranged on one side of each mounting frame (240), a driving motor (270) is arranged on one side of each bent rod (210), and a dust removal roller brush (280) is arranged on the output end of each driving motor (270) penetrating through each bent rod (210);
a control box (300), the control box (300) being disposed at a front surface of the track motor (120), the inner cavity of the control box (300) is provided with a processor (310), a receiving module (320), an image processing module (340), a deep learning classification module (350), a storage module (360) and a driving module (370), the processor (310) is electrically connected with the camera (250) and the receiving module (320) in an input mode, the receiving module (320) is electrically input and connected with the handheld terminal (330), the processor (310) is bidirectionally connected with the image processing module (340) and the deep learning classification module (350), the processor (310) is electrically connected with the memory module (360) and the driving module (370) in an input mode, the driving module (370) is electrically connected with the track motor (120), the driving motor (270), the servo motor (220) and the electric turntable (130) in an output mode.
2. The deep learning based image classification system according to claim 1, characterized in that: and mounting screw holes and mounting bolts matched with the mounting screw holes are arranged on two sides of the cross beam (100).
3. The deep learning based image classification system according to claim 1, characterized in that: and light supplement lamps (260) are arranged on two sides of the mounting rack (240).
4. The deep learning based image classification system according to claim 1, characterized in that: the handheld terminal (330) is one of a smart phone, a tablet computer, a notebook computer or a PC terminal.
5. The deep learning based image classification system according to claim 1, characterized in that: an independent power supply is arranged in the inner cavity of the control box (300), and a power supply interface is arranged on one side of the control box (300).
6. The deep learning based image classification system according to claim 1, characterized in that: the inner cavity of the control box (300) is also provided with a wireless transmission module, and the wireless transmission module is connected with an external display device.
CN202210181176.6A 2022-02-25 2022-02-25 Image classification system based on deep learning Withdrawn CN114542902A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210181176.6A CN114542902A (en) 2022-02-25 2022-02-25 Image classification system based on deep learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210181176.6A CN114542902A (en) 2022-02-25 2022-02-25 Image classification system based on deep learning

Publications (1)

Publication Number Publication Date
CN114542902A true CN114542902A (en) 2022-05-27

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210181176.6A Withdrawn CN114542902A (en) 2022-02-25 2022-02-25 Image classification system based on deep learning

Country Status (1)

Country Link
CN (1) CN114542902A (en)

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Application publication date: 20220527

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