[Paper Review] ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning

Md. Shahariar Nafiz, Shuvra Smaran Das+3 more|arXiv (Cornell University)|Feb 6, 2023

Municipal Solid Waste Management8 citations

TL;DR

ConvoWaste proposes a deep learning–based waste segregation system that classifies waste into bins and uses servo motors, sensors, GSM, and an Android app for remote control and notification, achieving 98% accuracy.

ABSTRACT

Nowadays, proper urban waste management is one of the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and boost the circular economy. This paper proposes a machine to segregate waste into different parts with the help of a smart object detection algorithm using ConvoWaste in the field of deep convolutional neural networks (DCNN) and image processing techniques. In this paper, deep learning and image processing techniques are applied to precisely classify the waste, and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an Android app in order to dump the separated waste in the desired place thanks to its automation properties. The use of this system can aid in the process of recycling resources that were initially destined to become waste, utilizing natural resources, and turning these resources back into usable products. Thus, the system helps fulfill the criteria of a circular economy through resource optimization and extraction. Finally, the system is designed to provide services at a low cost while maintaining a high level of accuracy in terms of technological advancement in the field of artificial intelligence (AI). We have gotten 98% accuracy for our ConvoWaste deep learning model.

Motivation & Objective

• Address urban waste management challenges by automating waste sorting to support a circular economy.
• Develop a DCNN-based classifier to accurately segregate waste into designated bins.
• Integrate hardware components (servo motors, ultrasonic sensors) for automatic bin filling and alerting mechanisms.
• Enable remote monitoring and control via an Android application.

Proposed method

• Apply deep convolutional neural networks and image processing for waste classification.
• Use a servo motor-based mechanism to place detected waste into the correct bin.
• Incorporate ultrasonic sensors to monitor bin waste levels and dual-band GSM for communication.
• Provide remote control and data access through an Android app.

Experimental results

Research questions

• RQ1 Can a DCNN-based model accurately classify common waste types for automated segregation?
• RQ2 How effectively can hardware actuation (servos) and sensors (ultrasonic, GSM) automate continuous waste sorting?
• RQ3 Does the integrated system support timely notifications and remote management to enhance recycling workflows?

Key findings

• The proposed ConvoWaste system achieves 98% accuracy on waste classification.
• Waste detection results drive servo-actuated sorting into appropriate bins.
• Ultrasonic sensors provide bin level monitoring and trigger notifications for waste disposal.
• Dual-band GSM enables communication for status updates and control.
• System architecture enables remote operation via an Android app.