CN111178329A - Big data collection method for garbage image - Google Patents
Big data collection method for garbage image Download PDFInfo
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- CN111178329A CN111178329A CN202010054601.6A CN202010054601A CN111178329A CN 111178329 A CN111178329 A CN 111178329A CN 202010054601 A CN202010054601 A CN 202010054601A CN 111178329 A CN111178329 A CN 111178329A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/30—Scenes; Scene-specific elements in albums, collections or shared content, e.g. social network photos or video
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/10—Waste collection, transportation, transfer or storage, e.g. segregated refuse collecting, electric or hybrid propulsion
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Abstract
The invention discloses a big data collection method of a garbage image, belonging to the technical field of data acquisition and processing and comprising the following steps: step S1: installing an infrared sensing module on the garbage classification box or on buildings around the garbage classification box or beside the garbage classification box, setting and defining a sensing area of the infrared sensing module, and sensing and monitoring residents entering or approaching the sensing area; step S2: the resident of falling rubbish is close to induction zone, then trigger infrared induction module, infrared induction module starts the video and inserts the module, and open garbage classification case delivery door etc., this rubbish image's big data collection method does not need the special messenger to operate, on duty, very big manpower, material resources and financial resources have been saved, it is low to have solved present artifical waste classification efficiency, use material identification technology to carry out the incomplete defect of intelligent classification, can provide data guarantee in the development in the aspect of artificial intelligence, the application of promotion artificial intelligence in the waste classification direction that can be faster.
Description
Technical Field
The invention relates to a big data collection method of a garbage image, belonging to the technical field of data acquisition and processing.
Background
In urban life operation, not only is the product output, but also the waste is increased along with the consumption and use of the product, and the existing waste and the waste which is useless are more biased to resources which are difficult to classify and utilize. Nowadays, environmental protection is more and more emphasized, and garbage classification is actively developed in various cities, but the living habits of residents cannot be changed at a glance, so that the work of government garbage classification is difficult. In addition to the policy of forcing and restricting the garbage classification of residents, how to use artificial intelligence to classify garbage more efficiently is also the focus of research in the work of garbage classification technology research in future, and the collection of large data of garbage images is more important.
The prior art has the defects:
the method comprises the steps of firstly, garbage classification is based on how to distinguish garbage, how to realize restraint and supervision on the way of putting garbage into residents, most of existing garbage identification adopts a material identification technology, few data samples of the aspect of a technical library are not attached to morphological characteristics of actual garbage in the putting process, the types of the put garbage are difficult to judge by utilizing data resources, judgment standards and specifications are difficult to provide for the behavior of putting the residents, and a large amount of garbage samples are urgently required to be collected to expand garbage classification comparison sample data so as to realize the subsequent garbage classification process.
Secondly, because the data acquisition, identification, judgment, storage and comparison of the garbage images and the information interaction between human and machines need to have certain intellectualization, and self-correction data management operation can be realized, a simple database system is difficult to meet the requirement, and now along with the development and the evolution of an information-based society, big data comes up at the same time, but the big data is rarely involved on the road for realizing the intelligent garbage classification management, so that the progress of garbage classification is slowly advanced.
Therefore, the invention provides a big data collection method of the garbage image to optimize the problems.
Disclosure of Invention
The invention mainly aims to solve the defects that the existing artificial garbage classification is low in efficiency and incomplete in intelligent classification by using a material recognition technology, and provides a big data collection method of a garbage image, which can provide data guarantee for the development of artificial intelligence and can promote the application of artificial intelligence in the garbage classification direction more quickly.
The purpose of the invention can be achieved by adopting the following technical scheme:
a big data collection method of a garbage image comprises the following steps:
step S1: installing an infrared sensing module on the garbage classification box or on buildings around the garbage classification box or beside the garbage classification box, setting and defining a sensing area of the infrared sensing module, and sensing and monitoring residents entering or approaching the sensing area;
step S2: triggering the infrared induction module when the residents pouring the garbage get close to the induction area, starting the video access module by the infrared induction module, and opening a delivery bin door of the garbage classification bin;
step S3: when the residents deliver classified garbage to the bin gate of the garbage classification box, the video access module starts to record videos of the garbage delivered by the residents and transmits the recorded video streams to the image acquisition module;
step S4: the image acquisition module analyzes and intercepts the video stream recorded by the video access module and transmits the acquired image signal to the image comparison module;
step S5: the image comparison module compares the images acquired by the image acquisition module according to an image comparison algorithm;
step S6: after the analysis of the image comparison module, if the residents correctly throw classified garbage, the image storage module stores the images acquired by the image acquisition module and uploads the images to the data server for archiving; closing a bin door of the garbage classification bin to finish garbage throwing;
step S7: through the analysis of the image comparison module, if the resident puts classified garbage incorrectly and the bin gate of the garbage classification bin is not closed, the whole image of the garbage put by the resident is projected on a display screen on the garbage classification bin, namely the resident is indicated to be incorrectly classified or the bin gate is not correctly put;
step S8: the image comparison module analyzes and compares the overall images of the garbage thrown by residents one by one, and then a single garbage image which meets the classification regulation is collected by the image collection module; the garbage is saved by the image saving module and uploaded to the data server for archiving, but the garbage thrown by the residents needs to be sorted and classified again, the circulation judgment is continued, and the step S3 is returned until the throwing is finished.
Preferably, in step S5, the image comparison algorithm uses the API interface provided by the third-party companies such as Baidu, Greenwich, etc. and the image provided by the data server for comparison.
Preferably, in step S1, at least two infrared sensing modules are installed, and must completely cover the whole garbage classification box, so as to perform comprehensive sensing and monitoring, and work in a monitoring state all the time.
Preferably, the infrared sensing module is electrically connected with the video access module, the video access module is electrically connected with the image acquisition module, and the image acquisition module is electrically connected with the image comparison module;
the image comparison module is electrically connected with the image storage module, and the image storage module is electrically connected with the data server.
Preferably, the video access module in step S2 uses the photoelectric signal generated by opening the bin door of the garbage classification bin as the trigger signal, and the video access module is accessed to the camera in the bin door of the garbage classification bin, which only shoots the garbage delivered by the resident this time.
Preferably, the infrared sensing module in step S1 is an infrared sensing device, and an HC human body infrared sensor is adopted, and its model is cikoku XKC-003K 4-M1.
Preferably, the video access module in step S2 is a face recognition camera, and the camera adopts a latitude-longitude WSD-2M-KDT-01 face recognition camera.
Preferably, a prompt warning device is further installed on the garbage classification box to judge whether garbage throwing is correct.
Preferably, the warning device may be a signal indicator light distinguished by color or a music signal indicator device distinguished by sound.
The invention has the beneficial technical effects that:
the method for collecting the big data of the garbage image combines the hardware structure of the existing garbage classification box, does not need a specially-assigned person to operate and watch, greatly saves manpower, material resources and financial resources, can monitor the throwing behavior and the throwing garbage type of residents and acquire image information, effectively ensures the uniqueness of stored images through an image comparison algorithm, and can effectively avoid data errors. The method has high efficiency and accuracy in data acquisition, provides data guarantee for the development of the garbage classification technology in the aspect of artificial intelligence, and can promote the development of the artificial intelligence in the garbage classification direction more quickly; the defects that the existing manual garbage classification is low in efficiency and incomplete in intelligent classification by using a material identification technology are overcome.
Drawings
FIG. 1 is a system diagram of a preferred embodiment of a method for big data collection of spam images in accordance with the present invention;
FIG. 2 is a flowchart of an algorithm of a big data collection method of a garbage image according to a preferred embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention more clear and definite for those skilled in the art, the present invention is further described in detail below with reference to the examples and the accompanying drawings, but the embodiments of the present invention are not limited thereto.
As shown in fig. 1 and fig. 2, the method for collecting big data of a spam image according to this embodiment includes the following steps:
step S1: installing an infrared sensing module on the garbage classification box or on buildings around the garbage classification box or beside the garbage classification box, setting and defining a sensing area of the infrared sensing module, and sensing and monitoring residents entering or approaching the sensing area;
step S2: triggering the infrared induction module when the residents pouring the garbage get close to the induction area, starting the video access module by the infrared induction module, and opening a delivery bin door of the garbage classification bin;
step S3: when the residents deliver classified garbage to the bin gate of the garbage classification box, the video access module starts to record videos of the garbage delivered by the residents and transmits the recorded video streams to the image acquisition module;
step S4: the image acquisition module analyzes and intercepts the video stream recorded by the video access module and transmits the acquired image signal to the image comparison module;
step S5: the image comparison module compares the images acquired by the image acquisition module according to an image comparison algorithm;
step S6: after the analysis of the image comparison module, if the residents correctly throw classified garbage, the image storage module stores the images acquired by the image acquisition module and uploads the images to the data server for archiving; closing a bin door of the garbage classification bin to finish garbage throwing;
step S7: through the analysis of the image comparison module, if the resident puts classified garbage incorrectly and the bin gate of the garbage classification bin is not closed, the whole image of the garbage put by the resident is projected on a display screen on the garbage classification bin, namely the resident is indicated to be incorrectly classified or the bin gate is not correctly put;
step S8: the image comparison module analyzes and compares the overall images of the garbage thrown by residents one by one, and then a single garbage image which meets the classification regulation is collected by the image collection module; the garbage is saved by the image saving module and uploaded to the data server for archiving, but the garbage thrown by the residents needs to be sorted and classified again, the circulation judgment is continued, and the step S3 is returned until the throwing is finished.
In the present embodiment, in step S5, the image comparison algorithm uses the image recognition API interface provided by the third-party company, such as hundredths, spaciousness, etc., and the image provided by the data server for comparison.
In this embodiment, in step S1, infrared induction module installs two at least, and one is used for one and is equipped with, and must completely cover whole waste classification case, and comprehensive response control, and work is in the monitor state always, realizes 24 hours unmanned on duty, and rubbish is put in to the convenient resident of 24 hours, makes things convenient for the resident, improves resident' S the experience sense of putting in rubbish for the resident does not produce the psychology of boredom.
In this embodiment, in step S7, the resident puts in rubbish on the tray in the waste classification case, and video access module records the video, and the image acquisition module gathers the graphical information of rubbish on the whole tray, after every piece of rubbish of single contrastive analysis of image contrast module, if it is incorrect to find a large amount of or individual rubbish classification, will project the rubbish image on the tray to the display screen on the waste classification case, and put in the door and do not close, still can remind by voice: "you are good, you throw garbage incorrectly, please re-sort, classify, thank you", remind pronunciation at least 2 times, resident need re-sort at this time, reclassify, need to continue to circulate and judge, return to step S3, until throwing finish.
Therefore, the big data collection method of the garbage image collects image data with correct garbage classification thrown by residents on one hand, and stores, updates and perfects an image database in a data server, and on the other hand, when the garbage thrown by the residents is wrong, captures a part of garbage images with correct classification in the garbage images on the whole tray, and can also collect the garbage images, so that the image database in the data server is improved. Therefore, the invention can comprehensively collect the garbage image information and provides data guarantee for the development of the garbage classification technology in the aspect of artificial intelligence.
In this embodiment, the infrared sensing module is electrically connected to the video access module, the video access module is electrically connected to the image acquisition module, and the image acquisition module is electrically connected to the image comparison module;
the image comparison module is electrically connected with the image storage module, and the image storage module is electrically connected with the data server.
In this embodiment, the video access module in step S2 adopts the photoelectric signal of opening the bin door of the trash sorting bin as the trigger signal, and the video access module is accessed to the camera of the trash sorting bin door that only shoots the garbage delivered by the resident this time.
In this embodiment, the infrared sensing module in step S1 is an infrared sensing device, and an HC human body infrared sensor is adopted, which is named as cikoku xiao XKC-003K 4-M1.
In this embodiment, the video access module in step S2 is a face recognition camera, and the camera adopts a latitude-longitude WSD-2M-KDT-01 face recognition camera.
In this embodiment, a prompt warning device is further installed on the garbage classification box to determine whether garbage placement is correct; the prompting and warning device can be a signal indicating lamp distinguished by colors or a music signal indicating device distinguished by sound. For example: if the garbage is correctly thrown, relaxed and pleasant music can be thrown; if the garbage throwing is incorrect, a harsh alarm bell can be played, so that not only can the residents throwing the garbage be reminded, but also the passing residents can be warned, and the passing residents can be supervised, rectified and improved;
if the surrounding environment of the garbage classification box is noisy, a signal indicator lamp can be used;
when the residents throw the garbage completely correctly, the signal indicator lamp displays a green light to indicate that the passage is smooth and the green light means all the way, and when the residents throw the garbage incorrectly, the signal indicator lamp displays a yellow light or a red light to indicate that the garbage is thrown wrongly and needs to be rectified and improved immediately.
The method for collecting the big data of the garbage image combines the hardware structure of the existing garbage classification box, does not need a specially-assigned person to operate and keep watch, greatly saves manpower, material resources and financial resources, can monitor the throwing behavior and the throwing garbage type of residents and acquire image information, effectively ensures the uniqueness of stored images through an image comparison algorithm, and can effectively avoid data errors. The method has high efficiency and accuracy in data acquisition, provides data guarantee for the development of the garbage classification technology in the aspect of artificial intelligence, and can promote the development of the artificial intelligence in the garbage classification direction more quickly; the defects that the existing manual garbage classification is low in efficiency and incomplete in intelligent classification by using a material identification technology are overcome.
If the big data collection method can be completed, not only can full artificial intelligence of garbage classification be realized, but also accurate classification of mixed garbage can be realized. The dry garbage is little in the current recovered garbage, no one is willing to recover under the condition that one person collects the garbage with limited energy, and manpower and material resources are wasted, so that the cost for recovering the garbage is far higher than the cost for reproduction. However, if the big data collection method of the garbage image is applied to the urban intelligent garbage classification system, if the garbage generated every day in the whole city is accurately classified, each class is separated, even if a piece of used paper towel is used, the quantity generated every day is enough to be loaded on a large truck, and the paper towel recovered in the way can be used for producing recycled paper. And the automatic identification and classification of the machine are adopted, the vehicles are recovered in a centralized manner, and the cost of the whole recovery process can be reduced. In other words: "garbage is just a resource in a misplaced place", we regard it as garbage, but a place of change may become a "baby". Therefore, various wastes are completely classified and treated, and finally, the complete recovery of the wastes is realized, so that the garbage-free city is realized.
The above description is only for the purpose of illustrating the present invention and is not intended to limit the scope of the present invention, and any person skilled in the art can substitute or change the technical solution of the present invention and its conception within the scope of the present invention.
Claims (9)
1. A big data collection method of a garbage image is characterized by comprising the following steps:
step S1: installing an infrared sensing module on the garbage classification box or on buildings around the garbage classification box or beside the garbage classification box, setting and defining a sensing area of the infrared sensing module, and sensing and monitoring residents entering or approaching the sensing area;
step S2: triggering the infrared induction module when the residents pouring the garbage get close to the induction area, starting the video access module by the infrared induction module, and opening a delivery bin door of the garbage classification bin;
step S3: when the residents deliver classified garbage to the bin gate of the garbage classification box, the video access module starts to record videos of the garbage delivered by the residents and transmits the recorded video streams to the image acquisition module;
step S4: the image acquisition module analyzes and intercepts the video stream recorded by the video access module and transmits the acquired image signal to the image comparison module;
step S5: the image comparison module compares the images acquired by the image acquisition module according to an image comparison algorithm;
step S6: after the analysis of the image comparison module, if the residents correctly throw classified garbage, the image storage module stores the images acquired by the image acquisition module and uploads the images to the data server for archiving; closing a bin door of the garbage classification bin to finish garbage throwing;
step S7: through the analysis of the image comparison module, if the resident puts classified garbage incorrectly and the bin gate of the garbage classification bin is not closed, the whole image of the garbage put by the resident is projected on a display screen on the garbage classification bin, namely the resident is indicated to be incorrectly classified or the bin gate is not correctly put;
step S8: the image comparison module analyzes and compares the overall images of the garbage thrown by residents one by one, and then a single garbage image which meets the classification regulation is collected by the image collection module; the garbage is saved by the image saving module and uploaded to the data server for archiving, but the garbage thrown by the residents needs to be sorted and classified again, the circulation judgment is continued, and the step S3 is returned until the throwing is finished.
2. The big data collection method of spam images according to claim 1, wherein: wherein the image comparison algorithm in step S5 uses the API interface for image recognition provided by the third party companies such as Baidu, Kuangshi, etc., and the image provided by the data server for comparison.
3. The big data collection method of spam images according to claim 1, wherein: in step S1, at least two infrared sensing modules are installed, and must completely cover the whole garbage classification box, so as to perform comprehensive sensing and monitoring, and work in a monitoring state all the time.
4. The big data collection method of spam images according to claim 1, wherein: the infrared induction module is electrically connected with the video access module, the video access module is electrically connected with the image acquisition module, and the image acquisition module is electrically connected with the image comparison module;
the image comparison module is electrically connected with the image storage module, and the image storage module is electrically connected with the data server.
5. The big data collection method of spam images according to claim 4, wherein: the video access module in the step S2 uses the photoelectric signal of opening the bin door of the garbage classification bin as a trigger signal, and the video access module is accessed into the camera of the garbage classification bin door only for shooting the garbage delivered by the residents this time.
6. The big data collection method of spam images according to claim 1, wherein: the infrared sensing module in the step S1 is an infrared sensing device, and an HC human body infrared sensor is adopted, and the model thereof is cico wound XKC-003K 4-M1.
7. The big data collection method of spam images according to claim 1, wherein: the video access module in the step S2 is a face recognition camera, and the camera adopts a latitude sensing WSD-2M-KDT-01 face recognition camera.
8. The big data collection method of spam images according to claim 1, wherein: and a prompt warning device is also arranged on the garbage classification box and used for judging whether garbage is thrown correctly or not.
9. The big data collection method of spam images according to claim 8, wherein: the prompting and warning device can be a signal indicating lamp distinguished by colors or a music signal indicating device distinguished by sound.
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