Analysis of Machine Learning Methods for Wildfire Security Monitoring with an Unmanned Aerial Vehicles

The article is about the methods of machine learning, designed for the detection of wildfires using unmanned aerial vehicles. In the article presented the review of machine learning methods, described the motivation part of machine learning usage and comparison of fire and smoke detection is made. The research was focused on machine learning application for monitoring task with a restrictions according to scenarios of a real monitoring. The results of experiments with demonstration of effectiveness of detection are presented in the conclusion part

Detección de incendios mediante identificación de humo con visión artificial en condiciones de iluminación variable

La detección de humo en áreas abiertas representa una gran dificultad para los medios convencionales para detección de incendios. Mientras que la mayoría de los dispositivos utilizados para monitorear la presencia de fuego, están diseñados para trabajar en contacto con alguno producto de la combustión, como la temperatura o la concentración de humo en el aire, las herramientas basadas en Visión Artificial aprovechan las características ópticas del fuego o del humo, permitiendo realizar el monitoreo y la detección de incendios a mayor distancia. Sin embargo, las condiciones de captura de las imágenes complica el proceso. Diferentes niveles de iluminación, condiciones climáticas, así como la presencia de otros objetos móviles reducen el nivel de exactitud de los algoritmos existentes para la detección de humo. El presente proyecto se enfoca en presentar una propuesta de algoritmo para detección de humo mediante Visión Artificial que afronta el problema de la variación en las detecciones debida a los cambios de iluminación ambiental. Con este propósito, se diseñó un algoritmo compuesto por distintas etapas que analizan las imágenes en busca de características estáticas o dinámicas del humo. El algoritmo propuesto es descrito en el quinto capítulo de este trabajo escrito. Inicialmente, parte de una etapa de pre-procesamiento que permite ajustar la resolución de las imágenes extraídas desde un video de entrada, balancear la iluminación de las imágenes y etiquetarlas para evaluar la herramienta. Posteriormente, se emplea una etapa que realiza la detección de movimiento, una de análisis de la dirección del movimiento, otra más para el análisis de la información obtenida en espacio de Wavelets y un par de etapas complementarias que analizan el color en espacio RGB y YCbCr. Finalmente, los resultados son evaluados por una etapa clasificadora basada en la herramienta AdaBoost, para realizar la toma de decisiones y notificar sobre una detección de incendio. El algoritmo propuesto es evaluado a partir de los criterios de exactitud Sensibilidad (el porcentaje de detecciones correctas realizadas) y Especificidad (el porcentaje de no- detecciones correctamente realizadas). Los resultados de exactitud descritos en el sexto capítulo del presente trabajo escrito, se contrastan con los obtenidos por otros algoritmos replicados a partir del estado del arte. A partir de los casos de prueba planteados para cada escenario de iluminación evaluado, se identificó una reducción en la variación de los resultados, es decir, el cambio en los porcentajes de sensibilidad y especificidad en diferentes condiciones de iluminación, es menor al obtenido por los algoritmos replicados

A novel image feature descriptor for SLM spattering pattern classification using a consumable camera

In selective laser melting (SLM), spattering is an important phenomenon that is highly related to the quality of the manufactured parts. Characterisation and monitoring of spattering behaviours are highly valuable in understanding the manufacturing process and improving the manufacturing quality of SLM. This paper introduces a method of automatic visual classification to distinguish spattering characteristics of SLM processes in different manufacturing conditions. A compact feature descriptor is proposed to represent spattering patterns and its effectiveness is evaluated using real images captured in different conditions. The feature descriptor of this work combines information of spatter trajectory morphology, spatial distributions, and temporal information. The classification is performed using support vector machine (SVM) and random forests for testing and shows highly promising classification accuracy of about 97%. The advantages of this work include compactness for representation and semantic interpretability with the feature description. In addition, the qualities of manufacturing parts are mapped with spattering characteristics under different laser energy densities. Such a map table can be then used to define the desired spatter features, providing a non-contact monitoring solution for online anomaly detection. This work will lead to a further integration of real-time vision monitoring system for an online closed-loop prognostic system for SLM systems, in order to improve the performance in terms of manufacturing quality, power consumption, and fault detection

End-userApplication for Early Forest Fire Detection and Prevention

n this paper, we describe a Web application that has been designed and implemented by Fulda University of Applied Sciences in the context of the ASPires project. The application extends the functionality available to Crisis Management Centers (CMC). Actual readings from sensors installed in the test areas, for example national parks, are made available to CMC personnel, as well as pictures from cameras that are either mounted on stationary observation towers or taken by Unmanned Aerial Vehicles (UAVs) in the area of an actual of supposed forest fire. Data are transmitted to the Aspires cloud and delivered swiftly to the Web application via an open interface. Furthermore, fire alarms raised by novel detection algorithms are forwarded automatically to the application. This clearly improves the potential for the early detection of forest fires in rural areas

Standard Interfaces and Protocols at Sensor Network and Cloud Level Definition

In this paper we presented full design of the system for monitoring forest which consists of cloud platform, sensor networks and mobile (drone) technologies for data collection and cameras. We first present the advanced design and structural model of an advanced system for monitoring of forest area. This model integrate sensor networks and mobile (drone) technologies for data collection and acquisition of those data at existing Crisis Management Information Systems (CMIS). Then we demonstrate the possibility to map different technological solutions and the main result was the definition of the set of standard interfaces and protocols for network interoperability

Improving human movement sensing with micro models and domain knowledge

Human sensing is concerned with techniques for inferring information about humans from various sensing modalities. Examples of human sensing applications include human activity (or action) recognition, emotion recognition, tracking and localisation, identification, presence and motion detection, occupancy estimation, gesture recognition, and breath rate estimation. The first question addressed in this thesis is whether micro or macro models are a better design choice for human sensing systems. Micro models are models exclusively trained with data from a single entity, such as a Wi-Fi link, user, or other identifiable data-generating component. We consider micro and macro models in two human sensing applications, viz. Human Activity Recognition (HAR) from wearable inertial sensor data and device-free human presence detection from Wi-Fi signal data. The HAR literature is dominated by person-independent macro models. The few empirical studies that consider both micro and macro models evaluate them with either only one data-set or only one HAR algorithm, and report contradictory results. The device-free sensing literature is dominated by link-specific micro models, and the few papers that do use macro models do not evaluate their micro counterparts. Given the little and contradictory evidence, it remains an open question whether micro or macro models are a better design choice. We evaluate person-specific micro and person-independent macro models across seven HAR benchmark data-sets and four learning algorithms. We show that person-specific models (PSMs) significantly outperform the corresponding person-independent model (PIM) when evaluated with known users. To apply PSMs to data from new users, we propose ensembles of PSMs, which are improved by weighting their constituent PSMs according to their performance on other training users. We propose link-specific micro models to detect human presence from ambient Wi-Fi signal data. We select a link-specific model from the available training links, and show that this approach outperforms multi-link macro models. The second question addressed in this thesis is whether human sensing methods can be improved with domain knowledge. Specifically, we propose expert hierarchies (EHs) as an intuitive way to encode domain knowledge and simplify multi-class HAR, without negatively affecting predictive performance. The advantages of EHs are that they have lower time complexity than domain-agnostic methods and that their constituent classifiers are statistically independent. This property enables targeted tuning, and modular and iterative development of increasingly fine-grained HAR. Although this has inspired several uses of domain-specific hierarchical classification for HAR applications, these have been ad-hoc and without comparison to standard domain-agnostic methods. Therefore, it remains unclear whether they carry a penalty on predictive performance. We design five EHs and compare them to the best-known domain-agnostic methods. Our results show that EHs indeed can compete with more popular multi-class classification methods, both on the original multi-class problem and on the EHs' topmost levels