This document proposes the design of an autonomous garbage collecting robot that uses the YOLOv3 deep learning model for object detection. It discusses creating a custom dataset of garbage items and using it to train YOLOv3 and CNN models for detecting garbage in images captured by the robot. It also describes the hardware components of the robot including Raspberry Pi, Arduino, ultrasonic sensor, DC motors and servomotors. The document compares the performance of YOLOv3 and CNN models and concludes that YOLOv3 has better accuracy, precision and F1 score for garbage detection.

1. Garbage Collecting Robot Using YOLOv3 Deep
Learning Model
Akhila Assis
B.Tech Student
Department of ECE
RSET, Kochi
Kerala, India
Email: akhilaazziz1@gmail.com
Aleena Rose Biju
B.Tech Student
Department of ECE
RSET, Kochi
Kerala, India
Email: aleenasbiju@gmail.com
Alisha N A
B.Tech Student
Department of ECE
RSET, Kochi
Kerala, India
Email: alishanaushad1@gmail.com
Amrutha Dhanadas
B.Tech Student
Department of ECE
RSET, Kochi
Kerala, India
Email: amruthaanu99@gmail.com
Nitheesh Kurian
Assistant Professor
Department of ECE
RSET, Kochi
Kerala, India
Email: nitheeshk@rajagiritech.edu.in
Abstract—Due to rapid rise in population, there is an increase
in the waste scattered and littered on the roads. Waste manage-
ment has become an issue of concern, now more than ever around
the world. There are instances where waste is deliberately left
on the roads due to the lack of workers or means available. The
hazardous nature of the work and the health risks it can pose for
people is also another concern. With the rise of smart cities, we
recognized the need to develop an autonomous garbage collecting
robot that monitors, identifies and collects garbage from the
environment accurately. For this purpose, we also conducted a
comparative study of different object detection algorithms and
evaluated them based on various parameters.
Keywords—Garbage collecting robot, object detection al-
gorithm, CNN, YOLOv3, Autodesk TinkerCAD, Autodesk
Fusion 360, Transfer Learning
I. INTRODUCTION
One of the biggest problems that the world currently faces
is waste management. The streets are often littered with waste
consisting of bottles, can, food packets etc. The existing waste
management methods lack efficiency or incur high costs. The
need for an efficient and economical waste collection method
is the need of the hour.
This paper proposes a virtual idea of an autonomous
garbage collecting robot model that surveys an area, identifies
garbage lying on ground and collects them into the machine’s
mounted garbage collector. This can be considered a feasible
solution for proper waste management.
The robot is constructed with a four wheel drive on a
flat chassis. Four wheels are attached to the robot using four
different DC motors.Raspberry Pi is connected to an ultrasonic
sensor, and an Arduino UNO. The ultrasonic sensor detects
978-1-6654-3919-0/21/$31.00 ©2021 IEEE
any obstacle in front of it. If an object is detected at a distance
of 20 cm or less from the ultrasonic sensor, the robot stops
moving, the object detection flag is set as high, and the image
capturing module is activated. The image of the object is
captured with the help of a webcam. Based on this image,in
order to detect and distinguish garbage from other objects,
YOLOv3 (You Only Look Once) real-time object detection
algorithm is used.
The arm of the robot is designed as a grabber which
lift objects detected by robots as garbage.There are 2 servos
at the grabber : the first servo is to clamp the object and the
second one is to lift the grabber. The grabber is connected to
the Arduino UNO.
The virtual model of the robot is implemented using
Autodesk TinkerCAD and Autodesk Fusion 360.
Most of the existing work to make a garbage collecting
robot includes object detection using only sensor fusion,
computer vision algorithm, Iot etc.
In one method [1], an autonomous robot is proposed
to collect waste from an environment.The robot tested in a
designed region performs tasks such as avoiding the static and
dynamic obstacles and sucking of waste mechanism.In order
to clean the entire room efficiently, a combination of S-shaped
algorithm and wall follow algorithm is used
In another method [2] to detect and segregate into bio-
degradable and non-biodegradable, a robot is designed in a
manner that would be able to collect these items from the
environment when they were placed at random.To lift the
objects from the floor and place the objects in a container
an arm mechanism is used.
In order to make improvements over the existing methods
[3],a new technical idea of robotic arm is proposed in another
paper by designing a robotic arm with 7 DOF (Degree of
2021
10th
International
Conference
on
Advances
in
Computing
and
Communications
(ICACC)
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978-1-6654-3919-0/21/$31.00
©2021
IEEE
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DOI:
10.1109/ICACC-202152719.2021.9708298
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2. Freedom). The working starts at arm envelope, space and
increasing its efficiency.By using different algorithms and
simulation,the system is enabled to add grip,hold and place
capability. The traditional methods of designing a robotic arm
will be outperformed by the proposed approach.
In another paper [4] using a modified You Only Look
Once v3 (YOLOv3) method a vision-based water surface
garbage capture robot has been developed.The detection scales
of YOLOv3 are simplified from 3 to 2 to improve the real-time
detection performance.The anchor boxes of the training data
set are reclustered for replacing some of the original YOLOv3
prior anchor boxes that are not appropriate to the data set.
Detection speed and accuracy of the modified YOLOv3 were
found to be better than that of other object detection algo-
rithms.
II. DATASET
With more than 500 handpicked images, a dataset cus-
tomized for garbage collection robot was created. Five classes
of garbage were chosen namely bottle, can, food packet,
paper ball, and plastic bag. These images were then labelled
manually using LabelImg. LabelImg is a graphical annotation
tool which is used for labelling object bounding boxes in
images. After labelling an image classes.txt file is generated
which contains all the classes which is annotated. For every
image labelled there will a corresponding .txt file which
includes the metadata. The metadata will include object id,
center x, center y, width, height.
III. METHODOLOGY
The whole project can be described three phases:
• Object Detection
• Garbage Identification
• Garbage Collection
The block diagram and flow chart of the proposed model is
shown in figure 1 and figure 2 respectively.
Fig. 1. Block diagram
A. Object Detection
The garbage collecting robot is movable and it moves until
the ultrasonic sensor detects another object. Ultrasonic sensor
is capable of detecting objects of about 20 cm distance and
Fig. 2. Flow Chart
when an object is detected it will stop the dc motor so that
the robot doesn’t move further. Webcam will get activated and
the image will be send to the Raspberry pi. Garbage detection
using deep learning mechanism is started.
B. Garbage Detection
Garbage detection is done using YOLOv3 algorithm. The
captured image is given to Raspberry pi. There it ensures
that the correct object is detected as garbage. A comparative
study is done for YOLOv3 and Convolutional Neural Network
(CNN).
1) You Only Look Once (YOLO v3): A modified YOLOv3
network detects the garbage lying on the ground. YOLOv3
training was done using Google Colab. The object detection
code written in python was given to Raspberry pi 3b+ and run.
The garbage were detected.Transfer learning method is used
for YOLOv3 Object detection.
The overall prediction probability is calculated using the
formula:
Ntp
Ntp + Nfp
Ntp is the total number of correctly detected objects.
Nfp is the total number of incorrectly detected objects.
• Total number of correctly detected objects = 63
• Total number of incorrectly detected objects = 4
• Total number of object detected = 67
Using the above formula , the prediction probability of
YOLO v3 is calculated and 94.02 % efficiency was obtained.
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3. 70 percent of images from the dataset was given for training
and 30 percent for testing.
2) Convolutional Neural Network (CNN) : Convolutional
Neural Network composes of various convolutional and pool-
ing layers. The input image is given to the first convolutional
layer and output is obtained as activation map. Filters are
applied to the convolutional layer which extract relevant
features from the input image to pass further. To minimize the
number of parameters, pooling layers are added. Before doing
the prediction, several convolutional and pooling layers are
added. The output layer in CNN is a fully connected layer. The
output is generated via output layer and a comparison is made
with error generation. This is then back propagated to update
the filter(weights) and bias values. For training and testing of
CNN, a dataset was created and the parameters were modified
for this project. The training was performed on Google Colab.
• Layer size is given as 64 and 3 convolutional layers were
used
• Filter used is 3x3 and the max pooling size is 2x2.
• In the dense layer, sigmoid activation function is used.
• Batch size = 32 and epoch = 100
• Validation split given as 70 percent for training and 30
percent for testing.
C. Garbage Collection
Using YOLOv3 algorithm, the Raspberry pi will transmit
a signal to the Arduino if the object detected is identified as
garbage. Arduino commence to operate the servo motors of
the garbage collecting robot. At first, the arm’s servo motor
causes the arms of the robot to go down and after that the
grabber servos rotate to collect the garbage. The arm’s servos
cause the arm to go upward and dispose the garbage to a bin
placed over the robot. If the object is not identified as garbage,
the robot continues to move forward.
IV. HARDWARE
A. Raspberry Pi 3B+
The Raspberry Pi is a mini computer board that comprises
of CPU, GPU, USB ports, I/O pins, WiFi, Bluetooth, USB
and network boot and power over ethernet facility. It is a
64 bit processor with 1GB RAM. It consists of Broadcom
BCM2837B0 chipset accompanied with with 1.4GHz Quad-
Core ARM Cortex-A53, 4 cores and consists of 40 pin header
(26 GPIOs). It has 4 USB, 2.0 ports Gigabit Ethernet and 2
pin set header. PoE (power over Ethernet) is a vital feature
included in the device.
B. Arduino Uno R3
Arduino Uno R3 is a microcontroller based on ATmega 328
that includes 14 digital I/O pins and 6 analog I/O pins.
• Operating Voltage : 5V
• Input voltage range : 7V to 12V
• Input voltage (limit) : 6V to 20 V
• DC Current for each I/O Pin : 20 mA
• DC Current used for 3.3V Pin : 50 mA.
• Flash Memory : -32 KB
• Speed of the CLK : 16 MHz
0.5 KB memory is used by the boot loader with SRAM 2 KB
and EEPROM 1 KB. It has an inbuilt LED.
C. Ultrasonic sensor
Ultrasonic sensor is a module which measures distance
using ultrasonic waves. The sensor head emits an ultrasonic
wave and receives the wave reflected back from the target.
It measures the distance to the target by measuring the time
between the emission and reception. To find the distance to
an object, the below mentioned formula can be used:
Distance = (speed of sound x time taken) / 2
• Supply voltage : 5V
• Global current consumption : 15 mA
• Ultrasonic Frequency : 40k Hz
• Maximal Range : 400 cm
• Minimal Range : 3 cm
• Resolution : 1 cm
• Trigger Pulse Width : 10 µs
• Outline Dimension : 43x20x15 mm
D. DC Motors
DC motor is a class of electrical machines that converts
direct current electrical power into mechanical power. The
most common types focus on the forces produced by magnetic
fields. In this project, two DC motors are used on the right
and the left each, giving a total of four.
E. Webcam
A webcam is a video camera that feeds or streams an image
or video in real time to or through a computer network,for
instance, the Internet. Webcam software helps users to record
a video or stream the video on the Internet. As video stream-
ing over the internet demands more bandwidth, compressed
formats are generally used.
F. Servomotors
A servomotor is a rotary actuator or linear actuator which
permits precise control of angular or linear position, velocity
and acceleration. It comprises of a suitable motor coupled to a
sensor for position feedback. This project uses 5 servo motors.
V. CIRCUIT SIMULATION OF ROBOT
The circuit simulation of the robot is done using a 3D
modelling online platform ’Autodesk Tinkercad’. The circuit
comprises of 5 servomotors and 4 DC motors. First servo
motor serves as the base of the structure followed by second
motor which is placed in the upper arm. Third and fourth
motor is placed in the lower arm and the final one serves as
gripper.
An ultrasonic distance sensor is used to calculate the
distance of the object to be detected. Threshold value of
distance is taken as 150 cm here. If an object is located at
a distance below 150 cm, four DC motors will start rotating
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4. simultaneously symbolizing the 4 wheels of the robot. When
an object is positioned at a distance beyond 150cm, rotation
of DC motor will stop and the servomotors will begin rotating
one after the other. This is to show the movement of the robotic
arm.
VI. AUTODESK FUSION 360
The three dimensional model of the garbage collecting robot
is done using Autodesk fusion 360. The model consists of
a base with four wheels and a robotic arm with grabber. It
also consist of five servo motors in which two servo motors
are placed in the lower arm and one each in gripper, upper
arm and the base. The servo motor used for this model is
Nema 23 servo motor. The Standard NEMA23 is 22.4Kgcm
holding torque. Rated for 200W AC servo applications up to
2000RPM.
VII. RESULT AND ANALYSIS
The comparison for different algorithms are listed in Table
1.
TABLE I
COMPARISON OF ALGORITHMS
Parameters YOLOv3 CNN
Accuracy 0.9341 0.849
Precision 0.9722 0.85
Recall 0.84 0.8491
F1 Score 0.9012 0.8512
The equation for Precision is given as:
TruePositives
TruePositives + FalsePositives
The equation for Recall is given as:
TruePositives
TruePositives + FalseNegatives
The equation for F1-score can thus be determined by:
2 ∗
Precision ∗ Recall
Precision + Recall
Based on these evaluation parameters, YOLOv3 is found to
have better detection than CNN. YOLOv3 has higher values
of precision, accuracy and F1 score but CNN has a greater
recall value.
TABLE II
CONFUSION MATRIX FOR YOLOV3 ALGORITHM
Classes Accuracy Precision Recall F1 Score
Plastic Bottle 0.9376 0.9131 0.8325 0.8709
Paper Ball 0.9149 0.9372 0.8662 0.9032
Can 0.9365 0.8922 0.8560 0.8737
Plastic bag 0.9642 0.9561 0.8753 0.9139
Food Packet 0.8567 0.8715 0.8393 0.8550
Fig. 3. Robotic circuit simulation
Fig. 4. 3D Model of the robot
Fig. 5. YOLOv3 Output
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5. Fig. 6. CNN Output
VIII. CONCLUSION
The proposed work was successfully implemented. Ob-
ject detection algorithms, YOLOv3 and CNN, were used
for garbage detection, for both image detection as well as
livestream. Transfer learning method was used for YOLOv3
algorithm in which dataset of five classes of garbage was cre-
ated and labelled using the LabellImg tool and training of the
dataset was done using Google colab. Finally testing was done
and got an accuracy of 0.9341%. For CNN algorithm same
set of dataset was used and acquired the accuracy of about
0.849%. These algorithms were evaluated based on different
parameters like Accuracy, Precision, Recall and F1 Score. A
virtual 3D model of the robot was created on Autodesk Fusion
360 platform. The circuit simulation was also done for the
robot in which the server motors and the DC motors move
based on object detection and object identification.
The current work only includes a virtual representation
of the robot. Therefore, the future goal involves bringing the
virtual model into a reality by making it. Garbage detection
can be evaluated using other image detection algorithms like
RCNN for better accuracy or speed. Also the number of
images in the dataset could be improved to increase accuracy.
The number of classes in the dataset can also be expanded
to improve the range of garbage that can be detected and col-
lected. Garbage segregation is also another possible expansion
of this project.
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