WO2022241597A1

WO2022241597A1 - Ai intelligent garbage identification and classification system and method

Info

Publication number: WO2022241597A1
Application number: PCT/CN2021/094012
Authority: WO
Inventors: 王艺臻
Original assignee: 海南师范大学; 海南锔元科技有限公司
Priority date: 2021-05-17
Filing date: 2021-05-17
Publication date: 2022-11-24

Abstract

Disclosed in the present application are an AI intelligent garbage identification and classification system and method. The AI intelligent garbage identification and classification method comprises: acquiring a scene image by training a Faster-RCNN model, inputting the scene image to the trained Faster-RCNN model, identifying a garbage category in a garbage station by using the trained Faster-RCNN model, determining real-time coordinates of garbage in the scene image, and determining a travel amount according to the real-time coordinates, so as to control, according to the travel amount, a robotic arm or other grabbing devices to grab the garbage, thereby implementing an intelligent sorting operation of the garbage in the station, without requiring a worker to perform a sorting operation. is the present application not only improves the sorting efficiency, but also prevents the harm caused by a harsh environment to the worker, is suitable for different scenarios, and effectively solves the technical problem of low efficiency of manual sorting of the garbage.

Description

An AI intelligent garbage identification and classification system and method

technical field

The technical field of garbage classification of the present invention, in particular, relates to an AI intelligent garbage identification and classification system and method.

Background technique

Nowadays, with the rapid development of my country's economy and the continuous acceleration of urbanization, the amount of household garbage "manufactured" per capita in cities in my country is increasing, and the total amount of garbage is too much.

technical problem

At present, most garbage stations are manually sorted, which consumes a lot of manpower in garbage disposal, and there are various threats to the health of workers in the harsh working environment, and the efficiency of manual sorting will be greatly compromised. , the garbage sorting rate is also very low.

technical solution

Embodiments of the present invention provide an AI intelligent garbage identification and classification system and method to solve the technical problem of low efficiency of manual garbage sorting.

Described technical scheme is as follows:

On the one hand, the present invention provides a kind of AI intelligent garbage identification classification method, comprises the following steps:

Build a pre-trained Faster-RCNN model; obtain image data sets including various garbage categories, and specify recognition objects, input the image data sets to the pre-trained Faster-RCNN model for training, and obtain trained Faster -RCNN model; obtain the scene image, input the scene image into the trained Faster-RCNN model, identify the garbage category in the scene image and determine the real-time coordinates of the garbage in the scene image; analyze the The real-time coordinates obtain the travel distance, grab the garbage according to the travel distance, and place the captured garbage at the designated position.

Preferably, said building the pre-trained Faster-RCNN model includes the following steps: building Conv layers for extracting feature maps of pictures, wherein said Conv layers include three layers of conv, pooling, and relu; building a region generation network layer, Use the area generation network layer to generate the detection frame, and initially extract the target candidate area in the picture; build the area pooling layer, obtain and analyze the feature map and the target candidate area, and then extract the candidate feature map; build the classification layer, and use the border regression to obtain the detection frame The final precise position, and the target category is determined through the candidate feature map.

Preferably, the training step of training the pre-trained Faster-RCNN model includes: preprocessing the pictures in the image data set; the pre-trained Faster-RCNN model tests the pre-processed pictures to obtain Test result; preset average accuracy rate AP threshold value, evaluate according to the test result, if the evaluation result is lower than the average accuracy rate AP threshold value, then modify the pre-trained Faster-RCNN model parameters until the test result reaches the average Accuracy rate AP threshold.

Preferably, the amount of travel includes X-axis travel, Y-axis travel and Z-axis travel, wherein the Z-axis travel is a preset value, and the X-axis travel and the Y-axis travel are based on The real-time coordinates are calculated.

Preferably, the calculation steps of the X-axis travel amount and the Y-axis travel amount calculated according to the coordinates include:

Set a reference coordinate point; calculate the X-axis offset and the Y-axis offset of the reference coordinate point and the real-time coordinates; multiply the X-axis offset and the Y-axis offset by the transformation Factor a to obtain the X-axis travel and the Y-axis travel.

On the other hand, the present invention also provides an AI intelligent garbage identification and classification system, including: a robotic arm subsystem, a conveyor belt, an image acquisition unit and a control module, the conveyor belt is used to transport garbage to be sorted; the image acquisition unit , used to acquire on-site images of garbage transported on the conveyor belt, and input the on-site images to the control module; the control module includes an identification unit, a training unit, a coordinate unit and a travel unit, and the identification unit is built with The pre-trained Faster-RCNN model is used to identify garbage categories; the training unit is used to obtain image data sets that include multiple garbage categories, and specify recognition objects, and input the image data sets of multiple garbage categories to the pre-trained The trained Faster-RCNN model is trained to obtain the trained Faster-RCNN model; the coordinate unit is used to obtain the real-time coordinates of the garbage in the scene image; the travel distance unit is used to determine the travel distance according to the coordinates , and transmit the amount of travel to the subsystem of the robotic arm; the subsystem of the robotic arm is used to grab garbage according to the amount of travel and place the grabbed garbage at a designated location.

Preferably, the control module further includes a reset unit, and the reset unit is used to control the control system of the robotic arm to return to the initial position after the captured garbage is placed in a designated position.

Preferably, the travel distance unit includes a preset subunit and a calculation subunit, the preset subunit is used to obtain a preset Z-axis travel distance, and the calculation subunit is used to calculate the X-axis according to the real-time coordinates Amount of travel and Y axis travel.

Preferably, the identification unit includes: a first construction subunit for constructing Conv layers for extracting feature maps of pictures, wherein the Conv layers include three layers of conv, pooling and relu; a second construction subunit for It is used to build the area generation network layer, use the area generation network layer to generate the detection frame, and initially extract the target candidate area in the picture; the third building subunit is used to build the area pooling layer, obtain and analyze the feature map and the target candidate area Afterwards, the candidate feature map is extracted; the fourth subunit is built, and the classification layer is built, and the final precise position of the detection frame is obtained by border regression, and the target category is determined through the candidate feature map.

Preferably, the training unit includes: a preprocessing subunit for preprocessing pictures in the image data set; a testing subunit for performing preprocessing on the preprocessed pictures with the pretrained Faster-RCNN model Test, to obtain the test result; the evaluation subunit is used to preset the average precision rate AP threshold, evaluate according to the test result, if the evaluation result is lower than the average precision rate AP threshold, then modify the pre-trained Faster-RCNN model parameters until the test result reaches the AP threshold.

Beneficial effect

It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

An AI intelligent garbage recognition and classification method provided in an embodiment of the present invention includes: acquiring a scene image by training a Faster-RCNN model, inputting the scene image into the trained Faster-RCNN model, using the trained The Faster-RCNN model identifies the garbage category in the garbage station, and determines the real-time coordinates of the garbage in the on-site image, and determines the travel distance according to the real-time coordinates, so as to control the mechanical arm or other grabbing devices to grab the garbage according to the travel distance, and realize The intelligent sorting of garbage in the station does not require workers to sort, which not only improves the efficiency of sorting, but also prevents the harm to workers in harsh environments, and is applicable to different scenarios, effectively solving the low efficiency of manual garbage sorting. question.

An AI intelligent garbage identification and classification system provided by an embodiment of the present invention has the same effect as the above-mentioned method, and details are not described here.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings that need to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without creative work. It should be understood that the specific implementation methods described in this specification are only for explaining the present invention, not for limit the invention.

Fig. 1 is a flow chart of a method for identifying and classifying AI intelligent garbage provided by an embodiment of the present invention;

Fig. 2 is the modular block diagram of a kind of AI intelligent garbage identification classification system provided by the embodiment of the present invention;

Fig. 3 is a schematic structural diagram of an AI intelligent garbage identification and classification system provided by an embodiment of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

Referring to Fig. 1, a kind of AI intelligent garbage identification classification method of the embodiment of the present invention, comprises the following steps:

Step 1. Build a pre-trained Faster-RCNN model. Faster RCNN was used in target detection tasks by Ross Girshick and He Kaiming et al. in 2016. Compared with traditional RCNN, Faster RCNN can complete efficient Networks) to complete the selection of candidate boxes. The Faster R-CNN network is divided into two parts, one is Region Proposal Network (RPN), the second is Fast R-CNN. Among them, RPN includes proposals and conv layers in the figure, and Fast R-CNN includes convolutional layers, ROI pooling and subsequent fully connected layers. Faster RCNN first inputs the entire image into CNN to extract feature maps of the image. Input the picture features to the RPN to get the feature information of the candidate frame. For the features extracted from the candidate frame, RPN uses a classifier to judge whether it belongs to the candidate frame of the target to be recognized, and further adjusts the position of the candidate frame belonging to a certain category with a regressor. Finally, the feature vector of the target frame and the picture is input to the Roi pooling layer, and then classified by the classifier to complete the task of target detection, and the final position of the target is obtained through bounding box regression.

Step 2. Obtain an image data set including various garbage categories, specify the recognition object, input the image data set to the pre-trained Faster-RCNN model for training, and obtain a trained Faster-RCNN model. The image data set of this embodiment includes multiple different types of garbage image groups, each garbage image group is used to train the Faster-RCNN model for identifying a certain type of garbage, and the recognition model is designated by the operator to identify the object, that is, the garbage image recognition model Named by the operator. After training with the data in the image data set, the Faster-RCNN model can identify the garbage categories contained in the image data set. For example, the image data set includes three types of garbage image groups: mineral water bottles, old clothes, and plastic bags. Each garbage image group is input into the pre-trained Faster-RCNN model in turn. After the training is completed, the Faster-RCNN model can recognize these three types types of garbage, and the more data in the garbage image group, the higher the recognition accuracy.

Step 3. Obtain on-site images, input the on-site images into the trained Faster-RCNN model, identify the types of garbage in the on-site images and determine the real-time coordinates of the garbage in the on-site images. The identification and classification method in this embodiment can be applied to a garbage station, a garbage recycling site, or a place where garbage is discarded and piled up, and the on-site image is a real-time picture of the place where garbage needs to be sorted. The Faster-RCNN model identifies the garbage category and determines the real-time coordinates of the garbage in the scene image. The Faster-RCNN model will generate a detection frame when recognizing the target, and the real-time coordinates are the coordinates of the center point of the detection frame when the recognition is completed.

Step 4: Analyze the real-time coordinates to obtain the travel distance, grab the garbage according to the travel distance, and place the captured garbage at the designated location. In this embodiment, the real-time coordinates are converted into the movement distance of the robot arm, so as to control the robot arm to grab the garbage, and put the garbage at a designated location according to the recognized garbage type. For example, if the identified garbage is a mineral water bottle, the robotic arm will move to the set position for placing the mineral water bottle after grabbing the garbage, so as to realize the sorting of the garbage and facilitate subsequent recycling.

The embodiment of the present invention realizes the intelligent sorting of garbage in the station without workers sorting operation, which not only improves the sorting efficiency, but also prevents the harm of workers from harsh environments, and is applicable to different scenarios, effectively solving the problem of manual sorting Garbage inefficient technical issues.

Further, in an AI intelligent garbage recognition and classification method according to an embodiment of the present invention, building a pre-trained Faster-RCNN model includes the following steps: building Conv layers for extracting feature maps of pictures, wherein Conv layers include conv, pooling, Three layers of relu; build the area generation network layer, use the area generation network layer to generate the detection frame, and initially extract the target candidate area in the picture; build the area pooling layer, obtain and analyze the feature map and the target candidate area, and then extract the candidate feature map; build In the classification layer, border regression is used to obtain the final precise position of the detection frame, and the target category is determined through the candidate feature map. In this embodiment, the Raspberry Pi is used to build the Faster-RCNN model. The Raspberry Pi is an ARM-based microcomputer motherboard, with SD/MocroSD card as the memory hard disk, and has all the basic functions of a PC. Features are designed. In this embodiment, the parameters of the Conv layer are set as follows: the size of the convolution kernel is 3*3, the step size is 1, the pad filling is 1, and the image is converted into a matrix. After the Conv layers are calculated, the obtained matrix is Feature maps (proposal feature maps); build a pooling layer, use the Roi Pooling layer to collect input feature maps and proposals, extract proposal feature maps, and send them to the fully connected layer to determine the target type; build a classification layer, use proposal feature maps Calculate the category of the proposal, determine the final precise position of the detection frame, and determine the target category through the candidate feature map.

Further, in an AI intelligent garbage recognition and classification method according to an embodiment of the present invention, the training step of training the pre-trained Faster-RCNN model includes: preprocessing the pictures in the image data set; the pre-trained Faster-RCNN model Test the preprocessed pictures and obtain the test results; preset the average accuracy AP threshold and evaluate according to the test results. If the evaluation result is lower than the average accuracy AP threshold, modify the pre-trained Faster-RCNN model parameters until the test The result reached the average precision rate AP threshold. Specifically, in this embodiment, the user can shoot or download the image data set from the Internet, or select the existing data set PASCALVOC as the data set for target detection, which contains about 10,000 images that have been marked and The picture with borders contains 20 categories, which are used to train the model to adjust the system parameters; the preprocessing of the image dataset adjusts all the pictures to a uniform size, and manually labels them. Here, labelimg is used to label and generate corresponding xml files. Wherein the threshold AP is a percentage less than 1, the closer the value is to 1, the better the effect, but the AP value of the current target detection model is probably between 40% and 50%. In this embodiment, the average accuracy AP threshold is set to 42%, use the Faster-RCNN model trained on the Raspberry Pi and test it with the data set. If the average accuracy rate AP threshold is lower than 42%, modify the parameters, and test the training results after training again. If the average accuracy rate is When the threshold AP is higher than or equal to 42%, you can start to input the pictures to be detected, and use the trained Faster-RCNN model for target recognition.

Further, in an AI intelligent garbage identification and classification method according to an embodiment of the present invention, the amount of travel includes X-axis travel, Y-axis travel and Z-axis travel, wherein the Z-axis travel is a preset value, and the X-axis travel The amount and the Y-axis travel amount are calculated according to the real-time coordinates. In this embodiment, the robot arm for grabbing garbage is initially at a fixed height, and returns to the initial position after each garbage grab. Therefore, the preset value of the Z-axis travel amount can be set according to the height of the robot arm and the garbage placement point , and perform manual calibration. The calculation steps of the X-axis travel amount and the Y-axis travel amount calculated according to the coordinates include: setting the reference coordinate point; calculating the X-axis offset and the Y-axis offset of the reference coordinate point and the real-time coordinate; and the Y-axis offset are multiplied by the transformation factor a to obtain the X-axis travel amount and the Y-axis travel amount. The reference coordinate point is the coordinate of the robot arm on the horizontal plane, which is also the origin coordinate. Subtract it from the real-time coordinate to obtain the X-axis offset and Y-axis offset. The transformation factor a is a constant, according to the actual grasping progress The amount is determined. For example, in actual operation, first adjust and determine the value of the transformation factor a, take the reference coordinates as (0,0), and the real-time coordinates as (x, y), then the X-axis offset and Y-axis offset are x and y, manually input the amount of travel to control the robotic arm to reach the top of the real-time coordinates, and then divide the X-axis travel and Y-axis travel by x and y respectively to obtain the transformation factor a in the two directions.

Referring to FIG. 2 and FIG. 3 , the embodiment of the present application also proposes an AI intelligent garbage identification and classification system, including a robotic arm subsystem 100 , a conveyor belt 300 , an image acquisition unit 400 and a control module 200 . The image acquisition unit 400 is used to acquire the on-site image of the garbage transported on the conveyor belt 300, and input the on-site image to the control module 200; the control module 200 includes a recognition unit 220, a training unit 210, a coordinate unit 230 and a travel distance unit 240, and the recognition unit A pre-trained Faster-RCNN model is built on the 220 to identify garbage categories; the training unit 210 is used to obtain image data sets including multiple garbage categories, and specify recognition objects, and input image data sets of multiple garbage categories to The pre-trained Faster-RCNN model is trained to obtain the trained Faster-RCNN model; the coordinate unit 230 is used to obtain the real-time coordinates of the garbage in the scene image; the amount of travel unit 240 is used to determine the amount of travel according to the coordinates, and the amount of travel Transmission to the robotic arm subsystem 100; the robotic arm subsystem 100 is used to grab the rubbish according to the amount of travel and place the grabbed rubbish at a designated location. The AI intelligent garbage identification and classification system of this embodiment uses the conveyor belt 300 to transport the garbage to be sorted. The image acquisition unit 400 and the robotic arm subsystem 100 are arranged above the conveyor belt 300 so that they are at the same position on the horizontal plane as much as possible. When the garbage is transported When the position of the image acquisition unit 400 and the robotic arm subsystem 100 is reached, the conveyor belt 300 temporarily stops running until the sorting is completed, and the control module 200 then controls the conveyor belt 300 to move forward. The sorting process is as follows: After the conveyor belt 300 transports the garbage to the position of the image acquisition unit 400 and the robotic arm subsystem 100, the image acquisition unit 400 takes pictures of the scene on the conveyor belt 300, and inputs the pictures of the scene to the recognition unit 220 and the coordinate unit 230 , then identify the garbage type by the identification unit 220 and obtain the real-time coordinates of the garbage by the coordinate unit 230, and the coordinate unit 230 sends the real-time coordinates to the travel distance unit 240, and the travel distance unit 240 determines the travel distance according to the real-time coordinates, and transmits the travel distance to The robotic arm subsystem 100, the robotic arm subsystem 100 grabs garbage according to the amount of travel and puts the grabbed garbage at a designated location to realize garbage sorting without the need for workers to sort operations, which not only improves the sorting efficiency, but also prevents The hazards of harsh environments to workers are understood, and it is applicable to different scenarios.

In this embodiment, the control module 200 is realized by the Raspberry Pi. A 3.7V 3800mAh mini battery is placed inside the Raspberry Pi to supply power to the Raspberry Pi system. The maximum output current is 1.4A, and the continuous use can guarantee about 8 hours. On the one hand, it can maintain the normal operation of the Raspberry Pi operating system when there is an accident in the power supply system. The image acquisition unit 400 adopts a high-definition camera, and the high-definition camera is fixed above the conveyor belt 300 through a fixing frame.

Further, in an AI intelligent garbage identification and classification system according to an embodiment of the present invention, the control module 200 also includes a reset unit, which is used to control the robotic arm control system to return to initial position.

Further, in an AI intelligent garbage identification and classification system according to an embodiment of the present invention, the travel distance unit 240 includes a preset subunit and a calculation subunit, the preset subunit is used to obtain a preset Z-axis travel distance, and the calculation subunit It is used to calculate the X-axis travel amount and the Y-axis travel amount based on the real-time coordinates.

Further, in an AI intelligent garbage recognition and classification system according to an embodiment of the present invention, the recognition unit 220 includes: a first building subunit for building Conv layers for extracting feature maps of pictures, wherein Conv layers include conv, pooling , relu three layers; the second building subunit is used to build the area generation network layer, use the area generation network layer to generate the detection frame, and initially extract the target candidate area in the picture; the third building subunit is used to build the area pooling layer , obtain and analyze the feature map and the target candidate area, and then extract the candidate feature map; the fourth subunit is built to build the classification layer, use the border regression to obtain the final precise position of the detection frame, and determine the target category through the candidate feature map.

Further, in an AI intelligent garbage recognition and classification system according to an embodiment of the present invention, the training unit 210 includes: a preprocessing subunit for preprocessing pictures in the image data set; a testing subunit for pre-training Faster -The RCNN model tests the preprocessed pictures to obtain the test results; the evaluation subunit is used to preset the average precision rate AP threshold, and evaluates according to the test results. If the evaluation result is lower than the average precision rate AP threshold, modify the pre-set Trained Faster-RCNN model parameters until the test result reaches the average precision rate AP threshold.

Above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be applied to the foregoing embodiments The technical solutions described in the examples are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the application.

Claims

An AI intelligent garbage identification and classification method is characterized in that it comprises the following steps:

Build a pre-trained Faster-RCNN model;

Obtaining an image data set comprising a variety of rubbish categories, and specifying a recognition object, inputting the image data set into the pre-trained Faster-RCNN model for training, and obtaining a trained Faster-RCNN model;

Obtain a live image, input the live image into the trained Faster-RCNN model, identify the garbage category in the live image and determine the real-time coordinates of the garbage in the live image;

Analyzing the real-time coordinates to obtain the travel distance, grabbing garbage according to the travel distance and placing the grabbed garbage at a designated location.
A kind of AI intelligent garbage identification classification method according to claim 1, is characterized in that, the Faster-RCNN model of described building pre-training comprises the following steps:

Build Conv layers for extracting feature maps of images, wherein the Conv layers include conv, pooling, and relu layers;

Build a region generation network layer, use the region generation network layer to generate a detection frame, and initially extract target candidate regions in the image;

Build a region pooling layer, obtain and analyze the feature map and the target candidate region, and then extract the candidate feature map;

Build a classification layer, use border regression to obtain the final precise position of the detection frame, and determine the target category through the candidate feature map.
A kind of AI intelligent garbage identification classification method according to claim 1, is characterized in that, the training step that the Faster-RCNN model of described pre-training is trained comprises:

Preprocessing the pictures in the image data set;

The pre-trained Faster-RCNN model tests the preprocessed picture to obtain test results;

Preset the average precision rate AP threshold, evaluate according to the test results, if the evaluation result is lower than the average precision rate AP threshold, modify the pre-trained Faster-RCNN model parameters until the test result reaches the average precision rate AP threshold.
The method for identifying and classifying AI intelligent garbage according to claim 1, wherein the amount of travel includes the amount of X-axis travel, the amount of Y-axis travel and the amount of Z-axis travel, wherein the amount of Z-axis travel is a preset Assuming a value, the X-axis travel amount and the Y-axis travel amount are calculated according to the real-time coordinates.
An AI intelligent garbage identification and classification method according to claim 4, wherein the calculation steps of the X-axis travel amount and the Y-axis travel amount calculated according to the coordinates include:

Set the reference coordinate point;

calculating the X-axis offset and the Y-axis offset of the reference coordinate point and the real-time coordinates;

The X-axis offset and the Y-axis offset are respectively multiplied by a conversion factor a to obtain the X-axis travel and the Y-axis travel.
An AI intelligent garbage identification and classification system, including a robotic arm subsystem, a conveyor belt, an image acquisition unit and a control module,

The conveyor belt is used to transport garbage to be sorted;

The image acquisition unit is used to acquire on-site images of garbage transported on the conveyor belt, and input the on-site images to the control module;

The control module includes a recognition unit, a training unit, a coordinate unit and a travel unit, and the recognition unit is equipped with a pre-trained Faster-RCNN model for identifying garbage categories; the training unit is used to obtain a variety of garbage category of image data sets, and specify the recognition object, input the image data sets of various types of garbage into the pre-trained Faster-RCNN model for training, and obtain the trained Faster-RCNN model; the coordinate unit is used to obtain The real-time coordinates of the garbage in the on-site image; the travel distance unit is used to determine the travel distance according to the coordinates, and transmit the travel distance to the robotic arm subsystem;

The robotic arm subsystem is used to grab garbage according to the amount of travel and place the grabbed garbage at a designated location.
An AI intelligent garbage identification and classification system according to claim 6, wherein the control module further includes a reset unit, and the reset unit is used to control the The mechanical arm control system returns to the initial position.
An AI intelligent garbage identification and classification system according to claim 6, wherein the travel distance unit includes a preset subunit and a calculation subunit, and the preset subunit is used to obtain a preset Z-axis travel The calculation subunit is used to calculate the X-axis travel distance and the Y-axis travel distance according to the real-time coordinates.
An AI intelligent garbage identification and classification system according to claim 6, wherein the identification unit comprises:

The first building subunit is used to build Conv layers for extracting feature maps of pictures, wherein the Conv layers include three layers of conv, pooling and relu;

The second building sub-unit is used to build the area generation network layer, use the area generation network layer to generate the detection frame, and initially extract the target candidate area in the picture;

The third building subunit is used to build a region pooling layer, acquire and analyze the feature map and target candidate regions, and then extract candidate feature maps;

The fourth building sub-unit is building a classification layer, using border regression to obtain the final precise position of the detection frame, and determining the target category through the candidate feature map.
An AI intelligent garbage identification and classification system according to claim 6, wherein the training unit comprises:

A preprocessing subunit, configured to preprocess the pictures in the image data set;

The test subunit is used for the pre-trained Faster-RCNN model to test the preprocessed picture to obtain test results;

The evaluation subunit is used to preset the average precision rate AP threshold, evaluate according to the test results, if the evaluation result is lower than the average precision rate AP threshold, then modify the pre-trained Faster-RCNN model parameters until the test results reach The average precision AP threshold.