VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually small size objects, which makes it more difficult to detect than large ones; Secondly, it is hard to distinguish false targets which resemble real traffic signs in complex street scenes without context information. To handle these problems, we propose a novel end-to-end deep learning method for traffic sign detection in complex environments. Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance. To comprehensively evaluate the proposed method, we do experiments on several traffic sign datasets as well as the general object detection dataset and the results have shown the effectiveness of our proposed method

Digital reality: a model-based approach to supervised learning from synthetic data

Hierarchical neural networks with large numbers of layers are the state of the art for most computer vision problems including image classification, multi-object detection and semantic segmentation. While the computational demands of training such deep networks can be addressed using specialized hardware, the availability of training data in sufficient quantity and quality remains a limiting factor. Main reasons are that measurement or manual labelling are prohibitively expensive, ethical considerations can limit generating data, or a phenomenon in questions has been predicted, but not yet observed. In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. By investigating the parameter space defined of such models, training data can be generated in a controlled way compared to data that was captured from real world situations. We propose the Digital Reality concept and demonstrate its potential in different application domains, including industrial inspection, autonomous driving, smart grid, and microscopy research in material science and engineering

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Assessment of cockpit interface concepts for data link retrofit

The problem is examined of retrofitting older generation aircraft with data link capability. The approach taken analyzes requirements for the cockpit interface, based on review of prior research and opinions obtained from subject matter experts. With this background, essential functions and constraints for a retrofit installation are defined. After an assessment of the technology available to meet the functions and constraints, candidate design concepts are developed. The most promising design concept is described in detail. Finally, needs for further research and development are identified

Usage of convolutional neural network ensemble for traffic sign recognition

Предлагается для распознавания дорожных знаков использовать ансамбль сверточных нейронных сетей, который является модификацией робастного метода распознавания на основе нейронных сетей глубокого обучения. Данный ансамбль повышает скорость работы робастного метода распознавания, а также позволяет увеличить быстродействие с сохранением высокой точности распознавания за счет удаления из набора данных значений, которые не представляют полезной нагрузки

Técnicas de inteligencia artificial aplicadas a sistemas de detección y clasificación de señales de tráfico.

Esta tesis, presentada como conjunto de artículos de investigación, estudia y analiza soluciones para los sistemas de detección y clasificación de señales de tráfico que suponen un reto en aplicaciones de la actualidad, como son la seguridad y asistencia en carretera a conductores, los coches autónomos, el mantenimiento de señalización vertical, o el análisis de escenas de tráfico. Las señales de tráfico constituyen un activo fundamental dentro de la red decarreteras porque su objetivo es ser fácilmente perceptible por los peatones y conductores para advertirles y guiarlos tanto de día como de noche. El hecho de que las señales estén diseñadas para ser únicas y tener características distinguibles, como formas simples y colores uniformes, implica que su detección y reconocimiento sea un problema limitado. Sin embargo, el desarrollo de un sistema de reconocimiento de señales en tiempo real aún presenta desafíos debido a los tiempos de respuesta, los cuales son cruciales para tomar decisiones en el entorno, y la variabilidad que presentan las imágenes de escenas de tráfico, que pueden incluir imágenes a distintas escalas, puntos de vista complicados, oclusiones, y diferentes condiciones de luz. Cualquier sistema de detección y clasificación de señales de tráfico debe hacer frente a estos retos. En este trabajo, se presenta un sistema de clasificación de señales de tráfico basado en aprendizaje profundo (Deep Learning). Concretamente, los principales componentes de la red neuronal profunda (Deep Neural Network) propuesta, son capas convolucionales y redes de transformaciones espaciales (Spatial Transformer Networks). Dicha red es alimentada con imágenes RGB de señales de tráfico de distintos países como Alemania, Bélgica o España. En el caso de las señales de Alemania, que pertenecen al dataset denominado German Traffic Sign Recognition Benchmark (GTSRB), la arquitectura de red y los parámetros de optimización propuestos obtienen un 99.71% de precisión, mejorando tanto al sistema visual humano como a todos los resultados previos del estado del arte, siendo además más eficiente en términos de requisitos de memoria. En el momento de redactar esta tesis, nuestro método se encuentra en la primera posición de la clasificación a nivel mundial. Por otro lado, respecto a la problemática de la detección de señales de tráfico, se analizan varios sistemas de detección de objetos propuestos en el estado del arte, que son específicamente modificados y adaptados al dominio del problema que nos ocupa para aplicar la transferencia de conocimiento en redes neuronales (transfer learning). También se estudian múltiples parámetros de rendimiento para cada uno de los modelos de detección con el fin de ofrecer al lector cuál sería el mejor detector de señales teniendo en cuenta restricciones del entorno donde se desplegará la solución, como la precisión, el consumo de memoria o la velocidad de ejecución. Nuestro estudio muestra que el modelo Faster R-CNN Inception Resnet V2 obtiene la mejor precisión (95.77% mAP), mientras que R-FCN Resnet 101 alcanza el mejor equilibrio entre tiempo de ejecución (85.45 ms por imagen) y precisión (95.15% mAP)

Inventory of ATT system requirements for elderly and disabled drivers and travellers

This Inventory of ATT System Requirements for Elderly and Disabled Drivers and Travellers is the product of the TELSCAN project’s Workpackage 3: Identification and Updating of User Requirements of Elderly and Disabled Travellers. It describes the methods and tools used to identify the needs of elderly and disabled (E&D) travellers. The result of this investigation is a summary of the requirements of elderly and disabled travellers using different modes of transport, including private cars, buses/trams, metros/trains, ships and airplanes. It provides a generic user requirements specification which can guide the design of all transport telematics systems. However, it is important to stress that projects should also capture a more detailed definition of user requirements for their specific application area or system