Pluggable drone imaging analysis framework for mob detection during open-air events

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Applications of Intelligent Vision in Low-Cost Mobile Robots

With the development of intelligent information technology, we have entered an era of 5G and AI. Mobile robots embody both of these technologies, and as such play an important role in future developments. However, the development of perception vision in consumer-grade low-cost mobile robots is still in its infancies. With the popularity of edge computing technology in the future, high-performance vision perception algorithms are expected to be deployed on low-power edge computing chips. Within the context of low-cost mobile robotic solutions, a robot intelligent vision system is studied and developed in this thesis. The thesis proposes and designs the overall framework of the higher-level intelligent vision system. The core system includes automatic robot navigation and obstacle object detection. The core algorithm deployments are implemented through a low-power embedded platform. The thesis analyzes and investigates deep learning neural network algorithms for obstacle object detection in intelligent vision systems. By comparing a variety of open source object detection neural networks on high performance hardware platforms, combining the constraints of hardware platform, a suitable neural network algorithm is selected. The thesis combines the characteristics and constraints of the low-power hardware platform to further optimize the selected neural network. It introduces the minimize mean square error (MMSE) and the moving average minmax algorithms in the quantization process to reduce the accuracy loss of the quantized model. The results show that the optimized neural network achieves a 20-fold improvement in inference performance on the RK3399PRO hardware platform compared to the original network. The thesis concludes with the application of the above modules and systems to a higher-level intelligent vision system for a low-cost disinfection robot, and further optimization is done for the hardware platform. The test results show that while achieving the basic service functions, the robot can accurately identify the obstacles ahead and locate and navigate in real time, which greatly enhances the perception function of the low-cost mobile robot

Human detection in real time with thermal camera using drones

We currently live in a world where technology predominates and advances very quickly, developing applications and devices that help us in daily life to improve our lives and solve everyday problems. One of these technologies that are in full development and evolution are the Drones, or also called RPAS (Remotely Piloted Aircraft System). These systems are unmanned aerial vehicles that offer us infinity applications to cover these needs and problems that we have on a daily basis. On the other hand, another technology that is in full development and that has been seen to have great potential for developing applications is Artificial Intelligence (AI). To quickly define AI, we say that it is the intelligence that humans develop with body, brain and mind but expressed by a machine, processor and software. Thanks to the fact that Drones can fly over places where humans cannot reach, through their cameras we can see what is in those areas, so one of the most useful applications in Drones is Search and Rescue operations. The main focus of this project is to merge RPAS with AI to develope a Search and Rescue application. For this, an executable software has been created for any computer, which will allow to detect people lost in the forest or other places, through a video, either through a Streaming on Youtube or a video saved locally, allowing detection both in real time as well as in deferred time, making the detection done by the machine and the human being able to do other functions while the search is done with the Drone. The objective is to use both, the thermal and visual cameras of a Drone, to record a video or stream the image and send it to the software so that, through Artificial Intelligence, if it finds a person, it detects the human and an alarm sounds. The software has been developed in Python, an open source, cross-platform programming language that can be used for web development, software creation, and data processing. This language is truly useful since it is one of the most used in the world of programming, thus there are multiple libraries, as OpenCV, created by open source users that have allowed the development of this human detection software. To develop the program, it has been necessary to train the machine with images of people. These images have been obtained with real flights in the area of Collserola, Barcelona. This area is close to the airport of Barcelona El Prat, so permissions and coordination are needed to be able to fly completely legally. For this reason, this project has also included all the documentation and legal part necessary to be able to fly in the Barcelona area

Assessing High Dynamic Range Imagery Performance for Object Detection in Maritime Environments

The field of autonomous robotics has benefited from the implementation of convolutional neural networks in vision-based situational awareness. These strategies help identify surface obstacles and nearby vessels. This study proposes the introduction of high dynamic range cameras on autonomous surface vessels because these cameras capture images at different levels of exposure revealing more detail than fixed exposure cameras. To see if this introduction will be beneficial for autonomous vessels this research will create a dataset of labeled high dynamic range images and single exposure images, then train object detection networks with these datasets to compare the performance of these networks. Faster-RCNN, SSD, and YOLOv5 were used to compare. Results determined Faster-RCNN and YOLOv5 networks trained on fixed exposure images outperformed their HDR counterparts while SSDs performed better when using HDR images. Better fixed exposure network performance is likely attributed to better feature extraction for fixed exposure images. Despite performance metrics, HDR images prove more beneficial in cases of extreme light exposure since features are not lost

CHARMIE: a collaborative healthcare and home service and assistant robot for elderly care

The global population is ageing at an unprecedented rate. With changes in life expectancy across the world, three major issues arise: an increasing proportion of senior citizens; cognitive and physical problems progressively affecting the elderly; and a growing number of single-person households. The available data proves the ever-increasing necessity for efficient elderly care solutions such as healthcare service and assistive robots. Additionally, such robotic solutions provide safe healthcare assistance in public health emergencies such as the SARS-CoV-2 virus (COVID-19). CHARMIE is an anthropomorphic collaborative healthcare and domestic assistant robot capable of performing generic service tasks in non-standardised healthcare and domestic environment settings. The combination of its hardware and software solutions demonstrates map building and self-localisation, safe navigation through dynamic obstacle detection and avoidance, different human-robot interaction systems, speech and hearing, pose/gesture estimation and household object manipulation. Moreover, CHARMIE performs end-to-end chores in nursing homes, domestic houses, and healthcare facilities. Some examples of these chores are to help users transport items, fall detection, tidying up rooms, user following, and set up a table. The robot can perform a wide range of chores, either independently or collaboratively. CHARMIE provides a generic robotic solution such that older people can live longer, more independent, and healthier lives.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The author T.R. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH). The author F.G. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) [grant number SFRH/BD/145993/2019], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH)

An Expert System for Weapon Identification and Categorization Using Machine Learning Technique to Retrieve Appropriate Response

In response to any terrorist attack on hospitals, airports, shopping malls, schools, universities, colleges, railway stations, passport offices, bus stands, dry ports and the other important private and public places, a proper plan will need to be developed effective response. In normal moments, security guards are deployed to prevent criminals from doing anything wrong. For example, someone is moving around with a weapon, and security guards are watching its movement through closed circuit television (CCTV). Meanwhile, they are trying to identify his weapon in order to plan an appropriate response to the weapon he has. The process of manually identifying weapons is man-made and slow, while the security situation is critical and needs to be accelerated. Therefore, an automated system is needed to detect and classify the weapon so that appropriate response can be planned quickly to ensure minimal damage. Subject to previous concerns, this study is based on the Convoluted Neural Network (CNN) model using datasets that are assembled on the YOLO and you only see once. Focusing on real-time weapons identification, we created a data collection of images of multiple local weapons from surveillance camera systems and YouTube videos. The solution uses parameters that describe the rules for data generation and problem interpretation. Then, using deep convolutional neural network models, an accuracy of 97.01% is achieved

Human motion estimation and analysing feasible collisions between individuals in real settings by using conventional cameras and Deep Learning-based algorithms

En este Trabajo Fin de Grado (TFG), se implementará un sistema de estimación y análisis del movimiento humano para la detección y prevención de posibles colisiones entre individuos que se desplazan libremente por un entorno industrial real. Se usarán dos o más cámaras convencionales situadas en puntos estratégicos del entorno a analizar y, mediante algoritmos de Deep Learning proporcionados mediante bibliotecas "open source", como MediaPipe, se estimarán las trayectorias seguidas y se predecirá la dirección del movimiento. Los resultados obtenidos se validarán utilizando, a su vez, cámaras RGB-D de bajo coste, que permitirán obtener medidas de distancia precisas a los individuos, permitiendo obtener el "ground truth" necesario para estimar el grado de precisión alcanzado en el sistema diseñado. El objetivo general que consiste en la estimación del movimiento humano y y el análisis de posibles colisiones entre individuos en entornos reales usando cámaras convencionales y algoritmos de Deep Learning, se conseguirá si se alcanzan los siguientes subobjetivos específicos: - Integración de los componentes de MediaPipe que permiten estimar los puntos de referencia para "Skeleton Tracking". - Diseño de una aplicación que integre los componentes diseñados para estimar las trayectorias de diferentes individuos a partir de la lectura de dos o más cámaras convencionales. - Diseño de una estrategia de predicción de la dirección de los individuos según la trayectoria seguida con el propósito de evitar colisiones. - Validación del sistema comparando los resultados obtenidos por las cámaras convencionales, con datos "ground truth" generados a partir de cámaras RGB-D que permitan medir fiablemente la distancia a los individuos y su posición en un sistema de referencia absoluto en tres dimensiones.Escuela Técnica Superior de Ingeniería IndustrialUniversidad Politécnica de Cartagen

Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from space

This article provides an example of the ways in which remote sensing, Earth observation, and machine learning can be deployed to provide the most up to date quantitative portrait of the South Asian ‘Brick Belt’, with a view to understanding the extent of the prevalence of modern slavery and exploitative labour. This analysis represents the first of its kind in estimating the spatiotemporal patterns in the Bull’s Trench Kilns across the Brick Belt, as well as its connections with various UN Sustainable Development Goals (SDGs). With a principal focus on Sustainable Development Goal Target 8.7 regarding the effective measures to end modern slavery by 2030, the article provides additional evidence on the intersections that exist between SDG 8.7 and those relating to urbanisation (SDG 11, 12), environmental degradation and pollution (SDG 3, 14, 15), and climate change (SDG 13). Our findings are then used to make a series of pragmatic suggestions for mitigating the most extreme SDG risks associated with brick production in ways that can improve human lives and human freedom