Intelligent garbage monitoring and sensing system and application thereof

Abstract

The invention relates to the technical field of scenic spot garbage monitoring and sensing, in particular to an intelligent garbage monitoring and sensing system and application thereof.The system comprises an acquisition module, a transmission module and a cloud recognition module, and the acquisition module is arranged in a to-be-sensed area at a fixed point, acquires video data in the to-be-sensed area and transmits the video data to the transmission module; the collected video data are uploaded to a cloud identification module through the transmission module, and the cloud identification module carries out target detection on the video data and pushes a garbage detection result of a to-be-sensed area and a distribution condition obtained through garbage detection to a client. According to the method, the garbage distribution condition in the specific area can be effectively sensed, the garbage cans and cleaners in the specific area can be effectively scheduled according to the acquired garbage distribution condition, the cleaning efficiency can be improved, and the sanitation condition in the specific area is ensured.

Description

Technical Field

[0001] The present invention relates to the field of scenic area garbage monitoring and sensing technology, and in particular to a garbage intelligent monitoring and sensing system and applications thereof. Background Art

[0002] Against the backdrop of rapid socio-economic development and a significant improvement in people's living standards, the tourism industry is entering a golden period, especially during the peak tourist season, when the flow of people in scenic spots surges, which not only brings economic prosperity, but also poses severe challenges to environmental management and data processing. Garbage disposal has become a major problem in scenic spots, especially in areas with high traffic, with frequent garbage accumulation, which seriously damages the environment of scenic spots and affects the experience of tourists. At present, garbage disposal in scenic spots relies on the traditional human inspection model, which is inefficient and costly. In addition, due to the lack of accurate positioning, it is difficult to fully cover the cleaning work. At the same time, although the internal systems of scenic spots are gradually improved, the data between the systems are isolated, forming information islands, which affects the integrated processing of data and restricts management efficiency and decision-making accuracy. In daily management, the collection of key data such as garbage distribution, positioning, and human behavior, despite the development of technology, has not fully utilized advanced communication technology. Once the existing network fails, data transmission is interrupted, resulting in a lack of real-time basis for management decisions, affecting the operation of scenic spots and the experience of tourists. In summary, scenic spots are facing multiple difficulties such as inefficient garbage disposal, data silos, and lack of data transmission reliability. They urgently need innovative solutions to achieve intelligent garbage management, integrated data processing, and ensure data transmission stability, optimize the scenic environment, enhance the tourist experience, and ensure the sustainable development of scenic spots. Summary of the invention

[0003] In view of the problems existing in the prior art, the present invention proposes a garbage intelligent monitoring and perception system applied to scenic spots, including a collection module, a transmission module, and a cloud recognition module, wherein: the collection module is fixedly set in the area to be perceived, collects video data in the area to be perceived, uploads the collected video data to the cloud recognition module through the transmission module, the cloud recognition module performs target detection on the video data, and pushes the garbage detection results of the area to be perceived and the distribution obtained by the garbage detection to the client.

[0004] Preferably, the acquisition module includes a plurality of cameras, and at least three cameras at different heights are arranged in the same monitoring area.

[0005] Preferably, the cloud recognition module tracks and recognizes the data collected by the camera, that is, tracks the targets identified as garbage and establishes a trajectory sequence for them.

[0006] Preferably, before the cloud recognition module performs tracking and recognition, the image of the original size is scaled by an adaptive image scaling algorithm to minimize black edge filling.

[0007] Preferably, during the tracking process of the cloud recognition module, the result obtained by recognizing each frame is matched based on the Hungarian algorithm to form a trajectory sequence of the recognized target.

[0008] The present invention also provides an application of a garbage intelligent monitoring and sensing system, which senses the type of garbage and the distribution of garbage in a specific area, and publishes tasks on an APP based on the acquired garbage distribution. After the user accepts the task, the garbage in the specific area is sent to a smart trash can, and the smart trash can gives points to the user based on the type of garbage.

[0009] Preferably, a camera is provided on the smart trash can, which captures the image of the trash and identifies it. After the identification is completed, the smart trash can opens a designated door, and closes the door when it is identified that the user has put the trash in.

[0010] Preferably, the smart trash can includes four separate buckets for separating recyclable garbage, hazardous garbage, dry garbage and wet garbage. The smart trash can is provided with a hatch, and a camera is provided on the hatch to identify the type of garbage to be put in and rotate the hatch to the corresponding separate bucket. When the hatch is opened, the user directly puts the identified garbage into the hatch, and the identified garbage is put into the corresponding type of separate bucket.

[0011] Compared with the existing technology, the present invention, on the one hand, utilizes artificial intelligence and image recognition technology to accurately capture the actions of tourists throwing garbage, and then transmits the data to the cloud algorithm through two-dimensional visual grid map technology and 5G communication to realize the model generation of accurate positioning of scenic area garbage, thereby improving work efficiency, and finally realizes intelligent classification and processing of garbage through external intelligent hardware devices; on the other hand, the present invention introduces a personal credit point and green point reward system to encourage more people to join environmental protection actions, stimulate environmental awareness, and reduce the generation of garbage from the root. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a preferred embodiment of the application of a garbage intelligent monitoring and sensing system of the present invention;

[0013] Figure 2 This is a schematic diagram of the distribution and positioning of the trash cans of the present invention;

[0014] Figure 3 This is a schematic diagram of garbage distribution identified by the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] The present invention proposes a garbage intelligent monitoring and perception system applied to scenic spots, comprising a collection module, a transmission module, and a cloud recognition module, wherein: the collection module is fixedly set in an area to be perceived, collects video data in the area to be perceived, uploads the collected video data to the cloud recognition module through the transmission module, the cloud recognition module performs target detection on the video data, and pushes the garbage detection result of the area to be perceived and the distribution obtained by the garbage detection to a client.

[0017] As an optional implementation scheme, firstly, an adaptive image scaling algorithm is used to calculate the optimal scaling ratio according to the original size of the input image to ensure that the scaled image matches the network input size (640x640) and minimizes black edge filling; then an adaptive anchor frame is used to obtain the recognition result.

[0018] As an optional implementation scheme, the target tracking algorithm workflow is as follows:

[0019] (1) Create the corresponding Tracks for the result detected in the first frame, initialize the motion variables of the Kalman filter, and predict the corresponding frame through the Kalman filter; at this time, the Tracks is unconfirmed.

[0020] (2) Perform IOU matching on the box detected in the current frame and the box predicted by Tracks in the previous frame, and then calculate the cost matrix (the calculation method is 1-IOU) based on the results of IOU matching.

[0021] (3) All the cost matrices obtained in (2) are used as the input of the Hungarian algorithm. There are three types of linear matching results. The first is Tracks mismatch (Unmatched Tracks), and the mismatched Tracks are deleted; the second is Detections mismatch (Unmatched Detections), and the Detections are initialized as a new Tracks (newTracks); the third is that the detection box and the predicted box are successfully paired, and the corresponding Detections are updated with their corresponding Tracks variables through Kalman filtering.

[0022] (4) Repeat steps (2) to (3) until confirmed tracks appear or the video frame ends.

[0023] (5) Predict the corresponding frames of the confirmed Tracks and unconfirmed Tracks through Kalman filtering. Perform cascade matching on the frames of the confirmed Tracks and Detections (previously, the appearance features and motion information of Detections were saved every time Tracks matched, and the first 100 frames were saved by default. The appearance features and motion information were used to perform cascade matching on Detections, and the possibility of matching confirmed Tracks and Detections was higher).

[0024] (6) There are three possible results after cascade matching. The first is Tracks matching, in which the corresponding Tracks variables are updated through Kalman filtering. The second and third are Detections and Tracks mismatches. In this case, the previously unconfirmed Tracks and mismatched Tracks are matched with the Unmatched Detections one by one through IOU matching, and then the cost matrix (the calculation method is 1-IOU) is calculated based on the results of IOU matching.

[0025] (7) All the cost matrices obtained in (6) are used as the input of the Hungarian algorithm to obtain the linear matching results. At this time, we get three kinds of results. The first is Tracks mismatch (Unmatched Tracks). We directly delete the mismatched Tracks (because this Track is in an uncertain state. If it is in a certain state, it must be deleted after a certain number of consecutive times (the default is 30 times)). The second is Detections mismatch (Unmatched Detections). We initialize such Detections as a new Tracks (new Tracks). The third is that the detection box and the predicted box are paired successfully, which means that we have successfully tracked the previous frame and the next frame, and update the corresponding Tracks variables through the Kalman filter.

[0026] (8) Repeat steps (5) to (7) until the end.

[0027] This embodiment proposes Figure 1An application method of a garbage intelligent monitoring and sensing system is shown, which captures the garbage distribution and tourist behavior in the area to be sensed through video, obtains the location information of the captured video by marking the camera position through the camera, and uploads the video captured by the camera to the cloud recognition module through the transmission module. In this embodiment, the cloud recognition module includes two modules, namely, a cloud platform AI module and a cloud platform database. The cloud platform AI module obtains the garbage distribution in the current area to be sensed based on the acquired video information and location information, and determines whether a new garbage bin needs to be placed based on the current distribution and the current distribution of smart garbage bins. In addition, environmental protection tasks can be issued within a certain range where there are smart garbage bins. After the user accepts the environmental protection task through the client, the user recycles the garbage into the designated garbage bin according to the instructions of the client.

[0028] As an optional implementation method, in this embodiment, the smart trash can includes four packaging barrels for recyclable garbage, hazardous garbage, dry garbage and wet garbage. The smart trash can is provided with a hatch. The hatch is provided with a camera for identifying the type of garbage to be put in and rotating the hatch to the corresponding packaging barrel. When the hatch is opened, the user directly puts the identified garbage into the hatch, and the identified garbage is put into the corresponding type of packaging barrel.

[0029] like Figures 2-3 As shown, the area to be sensed is gridded to monitor the number of trash cans and the distribution of trash in a specific area. The perception system proposed in this embodiment can effectively perceive the distribution of trash in the perception area and visualize the perception results. The managers of the area to be sensed can flexibly deploy trash cans based on the dynamic perception results. In addition, the pressure on cleaners can be reduced by publishing environmental protection tasks. In the process of users completing environmental protection tasks, they can also increase their awareness of trash classification by sliding with smart trash cans.

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A garbage intelligent monitoring and sensing system, characterized in that: It includes an acquisition module, a transmission module, and a cloud recognition module, wherein: the acquisition module is fixedly set in the area to be sensed, collects video data in the area to be sensed, uploads the collected video data to the cloud recognition module through the transmission module, the cloud recognition module performs target detection on the video data, and pushes the garbage detection result of the area to be sensed and the distribution obtained by the garbage detection to the client.

2. The intelligent garbage monitoring and sensing system according to claim 1 is characterized in that: The acquisition module includes a plurality of cameras, and at least three cameras at different heights are arranged in the same monitoring area.

3. The intelligent garbage monitoring and sensing system according to claim 1 is characterized in that: The cloud recognition module tracks and identifies the data collected by the camera, that is, it tracks the targets identified as garbage and establishes a trajectory sequence for them.

4. The intelligent garbage monitoring and sensing system according to claim 3 is characterized in that: Before the cloud recognition module performs tracking and recognition, the original size image is scaled using an adaptive image scaling algorithm to minimize black border padding.

5. The intelligent garbage monitoring and sensing system according to claim 3 is characterized in that: During the tracking process of the cloud recognition module, the results of each frame recognition are matched based on the Hungarian algorithm to form a trajectory sequence of the recognized target.

6. An application of a garbage intelligent monitoring and sensing system, characterized in that: According to claim 1, a garbage intelligent monitoring and sensing system senses the type of garbage and the distribution of garbage in a specific area, and publishes tasks on the APP based on the acquired garbage distribution. After the user accepts the task, the garbage in the specific area is sent to the smart trash can, and the smart trash can gives the user points based on the type of garbage.

7. The application of a garbage intelligent monitoring and sensing system according to claim 6 is characterized in that: A camera is installed on the smart trash can, which takes pictures of the garbage and identifies it. After the identification is completed, the smart trash can opens the designated door and closes the door when it recognizes that the user has put the garbage in.

8. The application of a garbage intelligent monitoring and sensing system according to claim 6 is characterized in that: The smart trash can includes four separate buckets for recyclable garbage, hazardous garbage, dry garbage and wet garbage. The smart trash can is provided with a hatch. The hatch is provided with a camera for identifying the type of garbage to be put in and rotating the hatch to the corresponding separate bucket. When the hatch is opened, the user directly puts the identified garbage into the hatch, and the identified garbage is put into the corresponding type of separate bucket.

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