Standalone Frequency Based Automated Trash Bin and Segregator of Plastic Bottles and Tin Cans

Sejera, Marloun; Ibarra, Joseph Bryan G.; Canare, Anrol Sarah; Escano, Lyra; Mapanoo, Dianne Claudinne; Suaviso, John Phillip

doi:10.1109/tencon.2016.7848454

Cited by 10 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The testing accuracy was 63% and 27%, respectively, for SVM and TrashNet, which have an architecture similar to AlexNet but with fewer and smaller filters. The TrashNet dataset was chosen and tested it with a variety of deep CNN architectures and optimization techniques [ 7 , 8 ]. By changing the connection patterns of the skip connections inside dense blocks, they aim to shorten prediction times based on the existing models.…”

Section: Introductionmentioning

confidence: 99%

A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

Gunaseelan,

Sundaram,

Mariyappan

2023

Sensors

View full text Add to dashboard Cite

A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system.

show abstract

Section: Introductionmentioning

confidence: 99%

A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

Gunaseelan,

Sundaram,

Mariyappan

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…This leads to 62 cubic meters of methane, which is equivalent to one ton of carbon dioxide (CO2) [5]. This underlines the vitality of a system that can contribute to recycling in terms of reducing plastic waste [6], [7]. In this methodology we developed Balsamiq Mockups, a tool to create prototypes of Web Interface and Mobile Application to simulate our recycling system [8], [9], [10].This Balsamiq Mockups methodology is used to develop an easy-to-use and fast web site and mobile application simulation, useful to communicate ideas [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

The progress of the environment in Peru: Design of a computer system for bottle recycling applied in shopping centers

Delgado¹

2020

IJETER

View full text Add to dashboard Cite

In this work, the aim is to explore the main problem in Peru, environmental pollution. The methodology used is Balsamiq Mockups, a prototype of web or mobile design. Regarding the case study, we will show the web design, the mobile application and the development of the prototype. Thus, the results of the initial days of launching the application in the respective shopping centers will be shown along with details of the daily benefit of recycling a bottle. The results will show the development during the initial days of use of the project in the shopping centers Plaza Norte and Mega Plaza; which at the same time will show the social interest of participation in the mentioned project.

show abstract

“…Near Infrared Spectroscopy is another technique used for the identification and selection of materials and is used for plastic segregation as proposed in [14]. Another common technique used in industries for material segregations is electrostatic separation as proposed in [12], but the major limitation of this process is that the materials should be made into granules and thus requires high infrastructural arrangements. All the existing sorting techniques encounter some snags like high capital and operation costs and are not economical for municipal solid waste management schemes.…”

Section: Introductionmentioning

confidence: 99%

A Low Cost Solution for Automatic Plastic Segregation

Dharmana,

2020

IJEAT

View full text Add to dashboard Cite

Abstract: Solid waste management is a universal issue that matters to every single person in the world. The solid waste management system is fundamentally labor intensive with very little collection efficiency. The available automatic plastic segregation techniques are based on thermal imaging and electrostatic properties of materials-these methods are expensive for governments to invest upon, and also to maintain in landfills. In this paper, artificial intelligence techniques are exploited to recognize the sounds of plastics from that of other materials by designing suitable mechanism to produce sound from debris during segregation, the segregation process can be automated with relatively low-cost electronics like System on Chips and audio sensors. With 30,000 recorded samples of noisy plastic and non-plastic material sounds, ANN is trained and was able to successfully detect plastics with 93.5% accuracy in real time. Algorithms were developed in python and real time testing was done on SoC with a mic, which affirms that the proposed method is cost effective when compared to techniques involving image processing, thermal imaging and near infrared spectroscopy.

show abstract

Standalone Frequency Based Automated Trash Bin and Segregator of Plastic Bottles and Tin Cans

Cited by 10 publications

References 0 publications

A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

A Design and Implementation Using an Innovative Deep-Learning Algorithm for Garbage Segregation

The progress of the environment in Peru: Design of a computer system for bottle recycling applied in shopping centers

A Low Cost Solution for Automatic Plastic Segregation

Contact Info

Product

Resources

About