CN115057139A - Automatic garbage can identification system and identification method used on garbage truck - Google Patents
Automatic garbage can identification system and identification method used on garbage truck Download PDFInfo
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- CN115057139A CN115057139A CN202210905121.5A CN202210905121A CN115057139A CN 115057139 A CN115057139 A CN 115057139A CN 202210905121 A CN202210905121 A CN 202210905121A CN 115057139 A CN115057139 A CN 115057139A
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- 238000006243 chemical reaction Methods 0.000 claims abstract description 12
- 238000001514 detection method Methods 0.000 claims description 35
- 238000012549 training Methods 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 7
- 238000013519 translation Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 4
- 238000009434 installation Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000011176 pooling Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 4
- 230000004888 barrier function Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 7
- 230000000007 visual effect Effects 0.000 abstract description 4
- 230000001960 triggered effect Effects 0.000 abstract description 2
- 239000000463 material Substances 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F3/00—Vehicles particularly adapted for collecting refuse
- B65F3/02—Vehicles particularly adapted for collecting refuse with means for discharging refuse receptacles thereinto
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
- B65F3/00—Vehicles particularly adapted for collecting refuse
- B65F3/02—Vehicles particularly adapted for collecting refuse with means for discharging refuse receptacles thereinto
- B65F2003/0223—Vehicles particularly adapted for collecting refuse with means for discharging refuse receptacles thereinto the discharging means comprising elements for holding the receptacle
- B65F2003/023—Gripper arms for embracing the receptacle
Abstract
The invention discloses an automatic garbage can recognition system and a recognition method used on a garbage truck, wherein the system comprises the garbage truck, a feeding mechanism, a clamp for clamping the garbage can, a vehicle-mounted industrial personal computer, a binocular camera, a controller and a signal conversion module; detecting the occurrence of a garbage can in a visual field in real time in the driving process of the garbage truck, carrying out early warning and marking the position of the garbage can in an image; the garbage bin is sent to a controller through a signal conversion module, the feeding mechanism is driven to extend out of the clamp to a designated position to hold the garbage bin, and the feeding mechanism dumps garbage after successful holding is detected; in the process of putting down the garbage can, if whether vehicles or pedestrians pass through the garbage can within the working radius of the vehicles is detected, an alarm signal is triggered, the working process is suspended, after the pedestrians or vehicles leave the working area, the garbage can is put down, the operation is completed, and the garbage can is convenient and rapid to use and high in automation degree.
Description
Technical Field
The invention relates to an automatic garbage can recognition system and a recognition method used on a garbage truck, and belongs to the technical field of automatic control of garbage trucks.
Background
The mode of material loading or side material loading after current garbage truck uses needs artifical auxiliary operation, and common embodiment is: the vehicle drives to the position near the garbage can to be cleaned and stops, a worker gets off to push the garbage can to be cleaned to the feeding mechanism, and the worker manually triggers the operations of dumping, placing the can and the like. The operation mode has the advantages of low automation degree, long operation period, high working strength of workers and severe operation environment.
In order to realize the automatic operation of the garbage truck, the position of the garbage can be acquired in a visual mode. In the normal driving process of the vehicle, the speed is high, so the requirement on real-time performance is high, but the calculated amount is large, the real-time performance is poor, the recognition rate is low, and the requirement of accurately recognizing the garbage bin placed on the roadside is difficult to realize.
Therefore, in order to develop an intelligent garbage can identification system, the problems of poor real-time performance and low identification rate of a visual scheme need to be solved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an automatic garbage can recognition system and a recognition method used on a garbage truck, which are used for finding a garbage can placed on the roadside, once the garbage can is found, the vehicle runs at a reduced speed, a detection module on the truck starts a signboard on the garbage can, when the garbage can is detected to be nearby, a program for accurately recognizing the garbage can is entered, a binocular camera arranged on the side surface of the garbage can recognizes the position coordinate of the garbage can relative to a feeding mechanism clamp, the coordinate is sent to a controller, the feeding mechanism is controlled to extend out of the clamp to hold the garbage can, the dumping is completed, and the system and the method are convenient and rapid to use and high in automation degree.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides an automatic trash can identification system for use on a trash vehicle, comprising:
the garbage truck comprises a feeding mechanism and a clamp for clamping the garbage can, wherein the clamp is arranged on the feeding mechanism;
the system comprises a vehicle-mounted industrial personal computer, a garbage truck and a control system, wherein the vehicle-mounted industrial personal computer is mounted on the garbage truck, and a target detection model is arranged in the vehicle-mounted industrial personal computer and used for detecting and obtaining position coordinates of a garbage can or pedestrians or vehicles relative to the garbage truck;
the system comprises a vehicle-mounted industrial personal computer, a binocular camera, a camera module and a control module, wherein the vehicle-mounted industrial personal computer is used for acquiring a color image and a depth image of a road surface on the side surface of a vehicle and uploading the color image and the depth image to the vehicle-mounted industrial personal computer;
the controller is connected with the feeding mechanism and used for driving the feeding mechanism to extend out of the clamp to a specified position to hold the garbage can;
and the signal conversion module is connected with the vehicle-mounted industrial personal computer and the controller.
In a second aspect, the present invention provides a method of identifying an automatic trash can identifying system for use on a garbage truck according to any one of the preceding claims, comprising:
acquiring a color image and a depth image acquired by a binocular camera, and inputting a pre-trained target detection model;
using a rectangular frame to mark the position of a garbage bin or a pedestrian or a vehicle in an image in the color image;
and receiving pixel coordinates returned by the rectangular frame and the corresponding classified vectors, and combining the collected depth images to obtain position coordinates of the garbage can or the pedestrians or the vehicles relative to the garbage truck, wherein the depth images are aligned with the color images.
Further, the training method of the target detection model includes:
acquiring garbage can pictures of the garbage truck in various environments;
marking a trash can, pedestrians or vehicles appearing in a picture by using a marking tool to manufacture a data set with a specific format, wherein the training set accounts for 70% of the total data volume, and the testing set accounts for 30% of the total data volume;
and training the standard detection model by using a training set to obtain the trained standard detection model, and verifying the accuracy of the model on the test set.
Further, the target detection network is obtained by modifying on the basis of a YOLO-V3 model, wherein a backbone network of the target detection network is Darknet53, a network structure from an input part to a part C0 is reserved in a detection task, average pooling, a full connection layer and softmax after C0 are removed, and a detection network module is added.
Further, the pose of the binocular camera, including the installation height of the camera and the Euler angle of the pose, is acquired by using a calibration tool, converted into a rotation matrix R and a translation matrix T, and stored locally, so that the three-dimensional point coordinates acquired by the binocular camera in a camera coordinate system are converted into point coordinates in a vehicle coordinate system.
Further, point cloud P is obtained according to a binocular camera 1 And converted into a point cloud P under a vehicle coordinate system through a formula c (ii) a Setting a height threshold value, filtering out points with too high or too low height, and obtaining a ground candidate point set P 2 (ii) a Dividing the candidate point set to obtain a ground point P g And model parameters M of the ground g ；
P c ＝RP 1 +T
Point cloud P c Removing the ground point P g Then obtaining the point cloud P 3 ，
P 3 ＝P C -P g
Screening out a candidate point P of the obstacle according to the height coordinate, the width coordinate and the distance coordinate of the point o The candidate points are points in a designated space in front of the garbage truck, then clustering is carried out according to the distances among the points, the points belonging to the same object are classified into one class to obtain a set of corresponding object points, a noise point set and a small obstacle point set are filtered according to the number of the points to obtain a point set of the obstacle, and the distance coordinate of the obstacle is calculated; and transmitting the detected obstacle information to the controller through the conversion module, and making corresponding early warning.
Further, the method comprises the following steps:
the garbage truck starts the back and opens the binocular camera automatically, gathers the place ahead road surface image, sends into on-vehicle industrial computer, obtains the relevant position information of vehicle, pedestrian, garbage bin and barrier through the identification of target detection model, passes to the controller through signal conversion module, controls the vehicle early warning to and control feed mechanism and grab a bucket operation automatically.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an automatic garbage can recognition system and a recognition method used on a garbage truck, wherein a binocular camera is installed in front of the garbage truck and used for finding a garbage can placed on the roadside, once the garbage can is found, the vehicle runs at a reduced speed, an RFID on the truck starts a signboard on the garbage can to be detected, when the garbage can is detected to be nearby, a program for accurately recognizing the garbage can is entered, the binocular camera installed on the side surface of the garbage can and a position coordinate of the garbage can relative to a feeding mechanism clamp are recognized, the coordinate is sent to a controller, the feeding mechanism is controlled to extend out of the clamp to hold the garbage can, the material dumping is completed, the system and the method are convenient and rapid to achieve high automation degree, the garbage can is detected through an optimized target detection model, and the recognition rate is higher.
Drawings
FIG. 1 is a hardware schematic block diagram of the present invention;
FIG. 2 is a flow chart of a semi-automated feeding method used on a garbage truck according to the present invention;
FIG. 3 is a schematic view of the apparatus of the present invention;
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
Example 1
The invention aims to disclose a system, which is applied to a garbage truck and is used for collecting a color image and a depth image of a road surface on the side surface of the garbage truck through a vehicle-mounted binocular camera arranged on the garbage truck; detecting the occurrence of a garbage can in a visual field in real time in the driving process of the garbage truck, carrying out early warning and marking the position of the garbage can in an image; the garbage bin clamping device is used for driving a feeding mechanism to extend out of a clamp to a specified position to clamp a garbage bin, and the feeding mechanism dumps garbage after detecting that the clamping is successful; in the process of putting down the garbage can, if whether vehicles or pedestrians pass through the working radius of the vehicles is detected, an alarm signal is triggered, the working process is suspended, and after the pedestrians or vehicles leave the working area, the garbage can is put down to finish the operation. The invention realizes the aim through the following technical scheme:
(1) binocular camera
The binocular camera is installed in the front of a feeding mechanism of the garbage truck, can emit infrared light at a position which is 100-150 cm away from the ground to form a light spot, can collect color images and depth images, and is internally provided with the temperature control module, so that the scheme can work in an environment of-30-60 degrees and can work under the condition of weak ambient light.
(2) Training convolutional neural network model
Acquiring garbage can pictures of the garbage truck in various environments, and marking the garbage can, pedestrians, vehicles and the like appearing in a picture by using a marking tool to manufacture a data set with a specific format; wherein the training set accounts for 70% of the total data volume and the test set accounts for 30% of the total data volume.
The convolutional neural network model used in the invention is a target detection model modified on the basis of YOLO-V3, wherein a backbone network is Darknet53, a network structure from input to a C0 part is reserved in a detection task, an average pooling, a full connection layer and softmax after C0 are removed, and a network module related to detection is added.
3) Detecting trash cans, pedestrians and vehicles
The trained target detection model can use a rectangular frame to mark the positions of the garbage can, the pedestrians, the vehicles and the like in the image, return pixel coordinates of the rectangular frame and vectors of corresponding classification, and combine with collected depth images (the depth images are aligned with the color images), so that the position coordinates of the garbage can, the pedestrians, the vehicles and the like relative to the vehicles can be obtained.
(4) Detecting obstacles at a certain height above ground
The binocular camera needs to be calibrated after being installed, the pose of the camera, including the installation height of the camera and the Euler angle of the pose, is acquired by using a calibration tool, converted into a rotation matrix R and a translation matrix T, stored locally, and used for converting three-dimensional point coordinates under a camera coordinate system acquired by the camera into point coordinates under a vehicle coordinate system. The scheme can realize the real-time ground segmentation function, and the method is as follows, the root binocular camera acquires the point cloud P 1 And converted into a point cloud P under a vehicle coordinate system through a formula c (ii) a Setting a height threshold value, filtering out points with too high or too low height, and obtaining a ground candidate point set P 2 (ii) a Dividing the candidate point set to obtain a ground point P g And model parameters M of the ground g 。
P c ＝RP 1 +T
Point cloudP c Removing the ground point P g Then obtaining the point cloud P 3 ，
P 3 ＝P C -P g
Screening out a candidate point P of the obstacle according to the height coordinate, the width coordinate and the distance coordinate of the point o Candidate points are points in a designated space in front of the vehicle, clustering is carried out according to the distances among the points, the points belonging to the same object are classified into one class, a set of corresponding object points is obtained, a noise point set and a small obstacle point set are filtered according to the number of the points, a point set of an obstacle is obtained, and the distance coordinate of the obstacle is calculated; and transmitting the detected obstacle information to the controller through the conversion module, and making corresponding early warning.
(5) Deployed on road-sweeping machines
The trained models are packaged and deployed on a vehicle-mounted industrial personal computer, a binocular camera is automatically opened after a vehicle is started, the front road surface image is collected and sent into the vehicle-mounted industrial personal computer for calculation, the vehicle-mounted industrial personal computer transmits the identified related position information of the vehicle, pedestrians, garbage cans and obstacles to a controller through a signal conversion module, vehicle early warning is controlled, and a feeding device is controlled to automatically grab the cans.
Example 2
The present embodiment provides an identification method of an automatic trash can identification system used on a garbage truck according to any one of embodiment 1, including:
acquiring a color image and a depth image acquired by a binocular camera, and inputting a pre-trained target detection model;
using a rectangular frame to mark the position of a garbage bin or a pedestrian or a vehicle in an image in the color image;
and receiving pixel coordinates returned by the rectangular frame and the corresponding classified vectors, and combining the collected depth images to obtain position coordinates of the garbage can or the pedestrians or the vehicles relative to the garbage truck, wherein the depth images are aligned with the color images.
The training method of the target detection model comprises the following steps:
acquiring garbage can pictures of the garbage truck in various environments;
marking a trash can, pedestrians or vehicles appearing in a picture by using a marking tool to manufacture a data set with a specific format, wherein the training set accounts for 70% of the total data volume, and the testing set accounts for 30% of the total data volume;
and training the standard detection model by using a training set to obtain the trained standard detection model, and verifying the accuracy of the model on the test set.
The target detection network is obtained by modifying on the basis of a YOLO-V3 model, wherein a backbone network of the target detection network is Darknet53, a network structure from an input part to a C0 part is reserved in a detection task, average pooling, a full connection layer and softmax after C0 are removed, and a detection network module is added.
And acquiring the pose of the binocular camera, including the installation height of the camera and the Euler angle of the pose, by using a calibration tool, converting the pose into a rotation matrix R and a translation matrix T, storing the rotation matrix R and the translation matrix T to the local, and converting the three-dimensional point coordinates acquired by the binocular camera in a camera coordinate system into point coordinates in a vehicle coordinate system.
Obtaining point cloud P from binocular camera 1 And converted into a point cloud P under a vehicle coordinate system through a formula c (ii) a Setting a height threshold value, filtering out points with too high or too low height, and obtaining a ground candidate point set P 2 (ii) a Dividing the candidate point set to obtain a ground point P g And model parameters M of the ground g ；
P c ＝RP 1 +T
Point cloud P c Removing the ground point P g Then obtaining the point cloud P 3 ，
P 3 ＝P C -P g
Screening out a candidate point P of the obstacle according to the height coordinate, the width coordinate and the distance coordinate of the point o The candidate points are points in a designated space in front of the garbage truck, then clustering is carried out according to the distances among the points, the points belonging to the same object are classified into one type to obtain a set of corresponding object points, a noise point set and a small obstacle point set are filtered according to the number of the points to obtain a point set of the obstacle, and the distance coordinate of the obstacle is calculated(ii) a And transmitting the detected obstacle information to the controller through the conversion module, and making corresponding early warning.
The garbage truck starts the back and opens the binocular camera automatically, gathers the place ahead road surface image, sends into on-vehicle industrial computer, obtains the relevant position information of vehicle, pedestrian, garbage bin and barrier through the identification of target detection model, passes to the controller through signal conversion module, controls the vehicle early warning to and control feed mechanism and grab a bucket operation automatically.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An automatic trash can identification system for use on a garbage truck, comprising:
the garbage truck comprises a feeding mechanism and a clamp for clamping the garbage can, wherein the clamp is arranged on the feeding mechanism;
the system comprises a vehicle-mounted industrial personal computer, a garbage truck and a control system, wherein the vehicle-mounted industrial personal computer is mounted on the garbage truck, and a target detection model is arranged in the vehicle-mounted industrial personal computer and used for detecting and obtaining position coordinates of a garbage can or pedestrians or vehicles relative to the garbage truck;
the system comprises a vehicle-mounted industrial personal computer, a binocular camera, a camera module and a control module, wherein the binocular camera is mounted in front of a feeding mechanism of the garbage truck, connected with the vehicle-mounted industrial personal computer and used for acquiring color images and depth images of a road surface on the side of a vehicle and uploading the color images and the depth images to the vehicle-mounted industrial personal computer;
the controller is connected with the feeding mechanism and used for driving the feeding mechanism to extend out of the clamp to a specified position to hold the garbage can;
and the signal conversion module is connected with the vehicle-mounted industrial personal computer and the controller.
2. The automatic trash can identification system for use on a trash vehicle of claim 1, wherein: and an alarm module is arranged in the vehicle-mounted industrial personal computer and used for triggering an alarm signal when detecting that vehicles or pedestrians pass through the inside of the operation radius of the garbage truck.
3. The automatic trash can identification system for use on a trash vehicle of claim 1, wherein: an infrared light emitting module is arranged in the binocular camera.
4. The automatic trash can identification system for use on a trash vehicle of claim 1, wherein: the inside of binocular camera is installed with temperature control module.
5. A method of identification of an automatic trash can identification system for use on a trash vehicle according to any one of claims 1-4, comprising:
acquiring a color image and a depth image acquired by a binocular camera, and inputting a pre-trained target detection model;
using a rectangular frame to mark the position of a garbage bin or a pedestrian or a vehicle in an image in the color image;
and receiving pixel coordinates returned by the rectangular frame and the corresponding classified vectors, and combining the collected depth images to obtain position coordinates of the garbage can or the pedestrians or the vehicles relative to the garbage truck, wherein the depth images are aligned with the color images.
6. The identification method according to claim 5, characterized in that: the training method of the target detection model comprises the following steps:
acquiring garbage can pictures of the garbage truck in various environments;
marking a trash can, pedestrians or vehicles appearing in a picture by using a marking tool to manufacture a data set with a specific format, wherein the training set accounts for 70% of the total data volume, and the testing set accounts for 30% of the total data volume;
and training the standard detection model by using a training set to obtain the trained standard detection model, and verifying the accuracy of the model on the test set.
7. The identification method according to claim 6, characterized in that: the target detection network is obtained by modifying on the basis of a YOLO-V3 model, wherein a backbone network of the target detection network is Darknet53, a network structure from an input part to a C0 part is reserved in a detection task, average pooling, a full connection layer and softmax after C0 are removed, and a detection network module is added.
8. The identification method according to claim 7, characterized in that: and acquiring the pose of the binocular camera, including the installation height of the camera and the Euler angle of the pose, by using a calibration tool, converting the pose into a rotation matrix R and a translation matrix T, storing the rotation matrix R and the translation matrix T to the local, and converting the three-dimensional point coordinates acquired by the binocular camera in a camera coordinate system into point coordinates in a vehicle coordinate system.
9. The identification method according to claim 8, characterized in that: obtaining point cloud P from binocular camera 1 And converted into a point cloud P under a vehicle coordinate system through a formula c (ii) a Setting a height threshold value, filtering out points with too high or too low height, and obtaining a ground candidate point set P 2 (ii) a Dividing the candidate point set to obtain a ground point P g And model parameters M of the ground g ；
P c ＝RP 1 +T
Point cloud P c Removing the ground point P g Then obtaining the point cloud P 3 ，
P 3 ＝P C -P g
Screening out a candidate point P of the obstacle according to the height coordinate, the width coordinate and the distance coordinate of the point o The candidate points are points in a designated space in front of the garbage truck, then clustering is carried out according to the distances among the points, the points belonging to the same object are classified into one class to obtain a set of corresponding object points, a noise point set and a small obstacle point set are filtered according to the number of the points to obtain a point set of the obstacle, and the distance coordinate of the obstacle is calculated; and transmitting the detected obstacle information to the controller through the conversion module, and making corresponding early warning.
10. The identification method according to claim 6, comprising:
the garbage truck starts the back and opens the binocular camera automatically, gathers the place ahead road surface image, sends into on-vehicle industrial computer, obtains the relevant position information of vehicle, pedestrian, garbage bin and barrier through the identification of target detection model, passes to the controller through signal conversion module, controls the vehicle early warning to and control feed mechanism and grab a bucket operation automatically.
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