Deep learning-based garbage bags and potholes detection model using Raspberry Pi

Given the current process for garbage collection and road maintenance, due to gaps in the pavement, in addition to the non-compliance of citizens with the norms established by entities for these services; city streets are becoming dirtier and less passable, affecting transportation. The main problem...

Full description

Autores:
Palacios Sánchez, Juan Felipe
Vitery Gómez, Santiago
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2021
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/53500
Acceso en línea:
http://hdl.handle.net/1992/53500
Palabra clave:
Ciudades inteligentes
Redes neuronales (Computadores)
Raspberry Pi (Computadora)
Aprendizaje automático (Inteligencia artificial)
Recolección de basuras
Ingeniería
Rights
openAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
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dc.title.eng.fl_str_mv Deep learning-based garbage bags and potholes detection model using Raspberry Pi
title Deep learning-based garbage bags and potholes detection model using Raspberry Pi
spellingShingle Deep learning-based garbage bags and potholes detection model using Raspberry Pi
Ciudades inteligentes
Redes neuronales (Computadores)
Raspberry Pi (Computadora)
Aprendizaje automático (Inteligencia artificial)
Recolección de basuras
Ingeniería
title_short Deep learning-based garbage bags and potholes detection model using Raspberry Pi
title_full Deep learning-based garbage bags and potholes detection model using Raspberry Pi
title_fullStr Deep learning-based garbage bags and potholes detection model using Raspberry Pi
title_full_unstemmed Deep learning-based garbage bags and potholes detection model using Raspberry Pi
title_sort Deep learning-based garbage bags and potholes detection model using Raspberry Pi
dc.creator.fl_str_mv Palacios Sánchez, Juan Felipe
Vitery Gómez, Santiago
dc.contributor.advisor.none.fl_str_mv Giraldo Trujillo, Luis Felipe
dc.contributor.author.none.fl_str_mv Palacios Sánchez, Juan Felipe
Vitery Gómez, Santiago
dc.contributor.jury.none.fl_str_mv Bressan, Michael
dc.subject.armarc.none.fl_str_mv Ciudades inteligentes
Redes neuronales (Computadores)
Raspberry Pi (Computadora)
Aprendizaje automático (Inteligencia artificial)
Recolección de basuras
topic Ciudades inteligentes
Redes neuronales (Computadores)
Raspberry Pi (Computadora)
Aprendizaje automático (Inteligencia artificial)
Recolección de basuras
Ingeniería
dc.subject.themes.none.fl_str_mv Ingeniería
description Given the current process for garbage collection and road maintenance, due to gaps in the pavement, in addition to the non-compliance of citizens with the norms established by entities for these services; city streets are becoming dirtier and less passable, affecting transportation. The main problem is that the entities in charge of these tasks do not have daily updated information. In the proposed article, a model for the detection of garbage bags and holes based on artificial vision and deep learning is proposed, which collects geographic information from garbage bags and holes present in the streets of a city. From this information a heat map is generated, which can be provided to the companies in charge of cleaning and maintaining the streets, and contribute to the progress towards a smart city. The behavior of the model has been explored and tested using a Raspberry Pi in real time, and the model has been shown to be fully functional and efficient. The overall performance of the proposed model has been achieved in terms of accuracy, precision and F1-score as 83%, 91% and 82% respectively.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-11-03T16:25:08Z
dc.date.available.none.fl_str_mv 2021-11-03T16:25:08Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Trabajo de grado - Pregrado
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dc.identifier.pdf.none.fl_str_mv 24475.pdf
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dc.format.extent.none.fl_str_mv 14 páginas
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dc.publisher.none.fl_str_mv Universidad de los Andes
dc.publisher.program.none.fl_str_mv Ingeniería Electrónica
dc.publisher.faculty.none.fl_str_mv Facultad de Ingeniería
dc.publisher.department.none.fl_str_mv Departamento de Ingeniería Eléctrica y Electrónica
publisher.none.fl_str_mv Universidad de los Andes
institution Universidad de los Andes
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spelling Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Giraldo Trujillo, Luis Felipevirtual::15651-1Palacios Sánchez, Juan Felipee7652d2e-50b1-4fae-95ca-697f5b1a50fc400Vitery Gómez, Santiago10f6a457-2871-4c17-8073-4c7cf61a8ada400Bressan, Michael2021-11-03T16:25:08Z2021-11-03T16:25:08Z2021http://hdl.handle.net/1992/5350024475.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Given the current process for garbage collection and road maintenance, due to gaps in the pavement, in addition to the non-compliance of citizens with the norms established by entities for these services; city streets are becoming dirtier and less passable, affecting transportation. The main problem is that the entities in charge of these tasks do not have daily updated information. In the proposed article, a model for the detection of garbage bags and holes based on artificial vision and deep learning is proposed, which collects geographic information from garbage bags and holes present in the streets of a city. From this information a heat map is generated, which can be provided to the companies in charge of cleaning and maintaining the streets, and contribute to the progress towards a smart city. The behavior of the model has been explored and tested using a Raspberry Pi in real time, and the model has been shown to be fully functional and efficient. The overall performance of the proposed model has been achieved in terms of accuracy, precision and F1-score as 83%, 91% and 82% respectively.Las calles de las ciudades se están volviendo más sucias y menos transitables debido al proceso de recolección de basuras y mantenimiento vial que se realiza actualmente. El principal problema es que las entidades encargadas de realizar estos procesos no tienen información actualizada diariamente. En el siguiente artículo se propone un modelo de detección de bolsas de basuras y huecos basado en visión artificial y deep learning, que recolecta información geográfica los objetos que se detectan. Con esta información, se genera un mapa de calor de las ciudades, que puede ser de gran utilidad para las compañías encargadas de hacer limpieza y mantenimiento de las calles, contribuyendo al progreso una ciudad inteligente. El desempeño del modelo se midió usando una Raspberry Pi, mostrando ser funcional y eficiente. La evaluación del modelo se realizó teniendo en cuenta las métricas accuracy, precisión y f1-score, obteniendo 83%, 91% y 82% respectivamente.Ingeniero ElectrónicoPregrado14 páginasapplication/pdfengUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaDeep learning-based garbage bags and potholes detection model using Raspberry PiTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TPCiudades inteligentesRedes neuronales (Computadores)Raspberry Pi (Computadora)Aprendizaje automático (Inteligencia artificial)Recolección de basurasIngeniería201616389Publicationhttps://scholar.google.es/citations?user=4TGvo8AAAAJvirtual::15651-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000802506virtual::15651-1eb386eec-3ec8-40c2-829d-ae8cbf0e384evirtual::15651-1eb386eec-3ec8-40c2-829d-ae8cbf0e384evirtual::15651-1THUMBNAIL24475.pdf.jpg24475.pdf.jpgIM Thumbnailimage/jpeg22120https://repositorio.uniandes.edu.co/bitstreams/104057b2-9cef-4fc1-8e9b-c8803b018c0f/download6d9f026591212ef428d2df893068e352MD55ORIGINAL24475.pdfapplication/pdf8087171https://repositorio.uniandes.edu.co/bitstreams/ee0f0e18-c1d5-445d-9c5d-dfe724b9d291/download924bdf03d7e3ee8fb8e36c468f3a4d34MD51TEXT24475.pdf.txt24475.pdf.txtExtracted texttext/plain23520https://repositorio.uniandes.edu.co/bitstreams/6036f3ae-7afa-4371-a8e2-4538344f4daf/downloadac62169322f2085f0877c2108c53acf9MD541992/53500oai:repositorio.uniandes.edu.co:1992/535002024-03-13 15:31:06.138https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co