46 research outputs found
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
IoT meets distributed AI ::deployment scenarios of Bonseyes AI applications on FIWARE
Bonseyes is an Artificial Intelligence (AI) platform composed of a Data Marketplace, a Deep Learning Toolbox, and Developer Reference Platforms with the aim of facilitating tech and non-tech companies a rapid adoption of AI as an enabler for their business. Bonseyes provides methods and tools to speed up the development and deployment of AI solutions on low power Internet of Things (IoT) devices, embedded computing systems, and data centre servers. In this work, we address the deployment and the integration of Bonseyes AI applications in a wider enterprise application landscape involving different applications and services. We leverage the well-established IoT platform FIWARE to integrate the Bonseyes AI applications into an enterprise ecosystem. This paper presents two AI application deployment and integration scenarios using FIWARE. The first scenario addresses use cases where edge devices have enough compute power to run the AI applications and there is only need to transmit the results to the enterprise ecosystem. The second scenario copes with use cases where an edge device may delegate most of the computation to an external/cloud server. Further, we employ FIWARE IoT Agent generic enabler to manage all edge devices related to Bonseyes AI applications. Both scenarios have been validated on concrete use cases and demonstrators
GSGS'18 ::3rd Gamification & Serious Game Symposium : health and silver technologies, architecture and urbanism, economy and ecology, education and training, social and politics
The GSGS’18 conference is at the interface between industrial needs and original answers by highlighting the playful perspective to tackle technical, training, ecological, management and communication challenges. Bringing together the strengths of our country, this event provides a solid bridge between academia and industry through the intervention of more than 40 national and international actors. In parallel with the 53 presentations and demos, the public will be invited to participate actively through places of exchange and round tables
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools
Next generation of embedded Information and Communication Technology (ICT)
systems are collaborative systems able to perform autonomous tasks. The
remarkable expansion of the embedded ICT market, together with the rise and
breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge
as it stands as one of the keys for the next technological revolution: the
seamless integration of AI in our daily life. However, training and deployment
of custom AI solutions on embedded devices require a fine-grained integration
of data, algorithms, and tools to achieve high accuracy. Such integration
requires a high level of expertise that becomes a real bottleneck for small and
medium enterprises wanting to deploy AI solutions on the Edge which,
ultimately, slows down the adoption of AI on daily-life applications. In this
work, we present a modular AI pipeline as an integrating framework to bring
data, algorithms, and deployment tools together. By removing the integration
barriers and lowering the required expertise, we can interconnect the different
stages of tools and provide a modular end-to-end development of AI products for
embedded devices. Our AI pipeline consists of four modular main steps: i) data
ingestion, ii) model training, iii) deployment optimization and, iv) the IoT
hub integration. To show the effectiveness of our pipeline, we provide examples
of different AI applications during each of the steps. Besides, we integrate
our deployment framework, LPDNN, into the AI pipeline and present its
lightweight architecture and deployment capabilities for embedded devices.
Finally, we demonstrate the results of the AI pipeline by showing the
deployment of several AI applications such as keyword spotting, image
classification and object detection on a set of well-known embedded platforms,
where LPDNN consistently outperforms all other popular deployment frameworks