A Computer Vision System to Localize and Classify Wastes on the Streets

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds

Automated home waste segregation and management system

Waste management is a massive issue in India, most of the present systems cannot manage waste on a scalable level, thus creating pressure on the ecosystem. Before the elimination of waste, segregation needs to be done to manage individual types of waste. Hence taken the same approach to solving the problem, which most of the present-day systems fail to do. The goal is to segregate the garbage generated in individual households into solid, liquid, biodegradable, non-biodegradable, combustible, and non-combustible, using many subsystems that involve electro pneumatics, compression, and storage. Image processing techniques will further advocate the process. The desired system will further reduce the waste of an in-built pulverizer. After conducting in-depth research on the present solutions for the urban waste processing chain, the level of complexity increases as the waste goes further along the chain and, in the end, the only option left is incineration was figured out. The solution allows endpoints of the chain to process different types of garbage in a more organized fashion. Municipal solid waste (MSW) is solid waste that results from municipal community, commercial, institutional, and recreational activities. This paper aims to segregate the MSW generated by households into biodegradable, non-biodegradable, combustible, and non-combustible

Identification of Garbage in the River Based on The YOLO Algorithm

This paper discusses the identification of garbage using the YOLO algorithm. In the rivers, it is usually difficult to distinguish between garbage and plants, especially when it is done in real-time and at the time of too much light. Therefore, there is a need for an appropriate method. The HSV and SIFT methods were used as preliminary tests. The tests were quite successful even in close conditions, however, there were still many problems faced in using this method since it is only based on pixel and shape readings. Meanwhile, the YOLO algorithm was able to identify garbage and water hyacinth even though they were closed to each other

Improving the performance of object detection by preserving label distribution

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.Comment: Code is available at https://github.com/leeheewon-01/YOLOstratifiedKFold/tree/mai

pLitterStreet: Street Level Plastic Litter Detection and Mapping

Plastic pollution is a critical environmental issue, and detecting and monitoring plastic litter is crucial to mitigate its impact. This paper presents the methodology of mapping street-level litter, focusing primarily on plastic waste and the location of trash bins. Our methodology involves employing a deep learning technique to identify litter and trash bins from street-level imagery taken by a camera mounted on a vehicle. Subsequently, we utilized heat maps to visually represent the distribution of litter and trash bins throughout cities. Additionally, we provide details about the creation of an open-source dataset ("pLitterStreet") which was developed and utilized in our approach. The dataset contains more than 13,000 fully annotated images collected from vehicle-mounted cameras and includes bounding box labels. To evaluate the effectiveness of our dataset, we tested four well known state-of-the-art object detection algorithms (Faster R-CNN, RetinaNet, YOLOv3, and YOLOv5), achieving an average precision (AP) above 40%. While the results show average metrics, our experiments demonstrated the reliability of using vehicle-mounted cameras for plastic litter mapping. The "pLitterStreet" can also be a valuable resource for researchers and practitioners to develop and further improve existing machine learning models for detecting and mapping plastic litter in an urban environment. The dataset is open-source and more details about the dataset and trained models can be found at https://github.com/gicait/pLitter

Implementación de una técnica de inteligencia artificial para el análisis de imágenes en búsqueda de la identificación de colillas de cigarrillos en áreas públicas

Trabajo de investigación tecnológicaLa contaminación del medio ambiente se ha convertido en un tema de estudio bastante importante en la actualidad debido a las diferentes crisis ambientales y de salubridad a las que se ha visto enfrentado el planeta. Los residuos de los cigarrillos son un agente de contaminación bastante importante y puede llegar a perjudicar una gran cantidad de recursos naturales que afectan directamente la vida cotidiana de las personas. Por este motivo durante este proyecto se buscó construir un aporte a esta problemática, con la implementación de técnicas de inteligencia artificial para la detección de colillas de cigarrillo en imágenes, con la intención de brindar un primer peldaño en el desarrollo de una solución que ayude a disminuir los niveles de contaminación en áreas públicas. Para conseguir el objetivo general del proyecto fue necesario el desarrollo de una vigilancia tecnológica con el fin de indagar en las tendencias actuales a nivel investigativo en temas que tienen relación con la detección de objetos en imágenes. Este proceso permitió seleccionar un modelo de Deep learning por medio del análisis bibliográfico de los documentos encontrados en la base de datos scopus. Se identifico una red de clústeres de palabras claves correlacionadas que genero una aproximación significativa con las palabras claves determinadas para el proyecto y a partir de esta red se decidió implementar un modelo de red neuronal convolucional conocido como Faster RCNN.INTRODUCCIÓN 1. GENERALIDADES 2. MARCO DE REFERENCIA 3. METODOLOGÍA 4. DESARROLLO DE LA PROPUESTA 5. CONCLUSIONES 6. TRABAJOS FUTUROS 7. BIBLIOGRAFÍAPregradoIngeniero de Sistema

A Multi-Level Approach to Waste Object Segmentation

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.Comment: Paper appears in Sensors 2020, 20(14), 381