TY - GEN
T1 - Design of a Convolutional Neural Network Based Smart Waste Disposal System
AU - Sunny, Md Samiul Haque
AU - Dipta, Debopriya Roy
AU - Hossain, Shifat
AU - Faruque, Hossain Mansur Resalat
AU - Hossain, Eklas
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In recent times, waste management problem has become a crucial challenge for Bangladesh, which is having a detrimental impact on the environment. This paper presents the proposition of designing a smart dustbin similar to an Automated Teller Machine (ATM) along with an intelligent embedded system, which has been dubbed as Automated Teller Dustbin (ATD). An efficient convolutional neural network (CNN) based image classifier is developed, which is able to detect and recognize any object regarded as garbage by analyzing training features. Additionally, it can also count the number of labeled objects and assign a price value to each object. The waste brought by any individual to the ATD will readily be recognized by the image classifier and the recycle value, which has been assigned for that object can be withdrawn by that individual. Therefore, a direct exchange of waste and its equivalent price is possible, which will incentivize people to use our proposed smart dustbin. After the installation cost, the operation and maintenance cost can be gained by recycling the garbage in it. A pre-trained CNN-based model ALexNet has been utilized to train and test the model with a dataset of 20 images for each of the 10 categorized objects collected from different waste management shops in Dhaka, Bangladesh. The model that has been trained for object recognition has attained an accuracy of 96%, which bears testimony to the feasibility of our proposal.
AB - In recent times, waste management problem has become a crucial challenge for Bangladesh, which is having a detrimental impact on the environment. This paper presents the proposition of designing a smart dustbin similar to an Automated Teller Machine (ATM) along with an intelligent embedded system, which has been dubbed as Automated Teller Dustbin (ATD). An efficient convolutional neural network (CNN) based image classifier is developed, which is able to detect and recognize any object regarded as garbage by analyzing training features. Additionally, it can also count the number of labeled objects and assign a price value to each object. The waste brought by any individual to the ATD will readily be recognized by the image classifier and the recycle value, which has been assigned for that object can be withdrawn by that individual. Therefore, a direct exchange of waste and its equivalent price is possible, which will incentivize people to use our proposed smart dustbin. After the installation cost, the operation and maintenance cost can be gained by recycling the garbage in it. A pre-trained CNN-based model ALexNet has been utilized to train and test the model with a dataset of 20 images for each of the 10 categorized objects collected from different waste management shops in Dhaka, Bangladesh. The model that has been trained for object recognition has attained an accuracy of 96%, which bears testimony to the feasibility of our proposal.
KW - Automated Teller Dustbin
KW - Convolutional Neural Network
KW - Object detection
KW - Object recognition
KW - Waste management
UR - http://www.scopus.com/inward/record.url?scp=85078567771&partnerID=8YFLogxK
U2 - 10.1109/ICASERT.2019.8934633
DO - 10.1109/ICASERT.2019.8934633
M3 - Conference contribution
AN - SCOPUS:85078567771
T3 - 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019
BT - 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Advances in Science, Engineering and Robotics Technology, ICASERT 2019
Y2 - 3 May 2019 through 5 May 2019
ER -