A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Violation of Traffic Rules and Detection of Sign Boards

Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used

Artificial intelligence enabled automatic traffic monitoring system

The rapid advancement in the field of machine learning and high-performance computing have highly augmented the scope of video-based traffic monitoring systems. In this study, an automatic traffic monitoring system is proposed that deploys several state-of-the-art deep learning algorithms based on the nature of traffic operation. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to track congestion, detect traffic anomalies and tabulate vehicle counts. To monitor traffic queues, this study implements a Mask region-based convolutional neural network (Mask R-CNN) that predicts congestion using pixel-level segmentation masks on classified regions of interest. Similarly, the model was used to accurately extract traffic queue-related information from infrastructure mounted video cameras. The use of infrastructure-mounted CCTV cameras for traffic anomaly detection and verification is further explored. Initially, a convolutional neural network model based on you only look once (YOLO), a popular deep learning framework for object detection and classification is deployed. The following identification model, together with a multi-object tracking system (based on intersection over union -- IOU) is used to search for and scrutinize various traffic scenes for possible anomalies. Likewise, several experiments were conducted to fine-tune the system's robustness in different environmental and traffic conditions. Some of the techniques such as bounding box suppression and adaptive thresholding were used to reduce false alarm rates and refine the robustness of the methodology developed. At each stage of our developments, a comparative analysis is conducted to evaluate the strengths and limitations of the proposed approach. Likewise, IOU tracker coupled with YOLO was used to automatically count the number of vehicles whose accuracy was later compared with a manual counting technique from CCTV video feeds. Overall, the proposed system is evaluated based on F1 and S3 performance metrics. The outcome of this study could be seamlessly integrated into traffic system such as smart traffic surveillance system, traffic volume estimation system, smart work zone management systems, etc.by Vishal MandalIncludes bibliographical reference

LiDAR-based Semantic Labeling : Automotive 3D Scene Understanding

Mobile Roboter und autonome Fahrzeuge verwenden verschiedene Sensormodalitäten zur Erkennung und Interpretation ihrer Umgebung. Neben Kameras und RaDAR Sensoren repräsentieren LiDAR Sensoren eine zentrale Komponente für moderne Methoden der Umgebungswahrnehmung. Zusätzlich zu einer präzisen Distanzmessung dieser Sensoren, ist ein umfangreiches semantisches Szeneverständnis notwendig, um ein effizientes und sicheres Agieren autonomer Systeme zu ermöglichen. In dieser Arbeit wird das neu entwickelte LiLaNet, eine echtzeitfähige, neuronale Netzarchitektur zur semantischen, punktweisen Klassifikation von LiDAR Punktwolken, vorgestellt. Hierfür finden die Ansätze der 2D Bildverarbeitung Verwendung, indem die 3D LiDAR Punktwolke als 2D zylindrisches Bild dargestellt wird. Dadurch werden Ergebnisse moderner Ansätze zur LiDAR-basierten, punktweisen Klassifikation übertroffen, was an unterschiedlichen Datensätzen demonstriert wird. Zur Entwicklung von Ansätzen des maschinellen Lernens, wie sie in dieser Arbeit verwendet werden, spielen umfangreiche Datensätze eine elementare Rolle. Aus diesem Grund werden zwei Datensätze auf Basis von modernen LiDAR Sensoren erzeugt. Durch das in dieser Arbeit entwickelte automatische Verfahren zur Datensatzgenerierung auf Basis von mehreren Sensormodalitäten, speziell der Kamera und des LiDAR Sensors, werden Kosten und Zeit der typischerweise manuellen Datensatzgenerierung reduziert. Zusätzlich wird eine multimodale Datenkompression vorgestellt, welche ein Kompressionsverfahren der Stereokamera auf den LiDAR Sensor überträgt. Dies führt zu einer Reduktion der LiDAR Daten bei gleichzeitigem Erhalt der zugrundeliegenden semantischen und geometrischen Information. Daraus resultiert eine erhöhte Echtzeitfähigkeit nachgelagerter Algorithmen autonomer Systeme. Außerdem werden zwei Erweiterungen zum vorgestellten Verfahren der semantischen Klassifikation umrissen. Zum einen wird die Sensorabhängigkeit durch Einführung des PiLaNets, einer neuen 3D Netzarchitektur, reduziert indem die LiDAR Punktwolke im 3D kartesischen Raum belassen wird, um die eher sensorabhängige 2D zylindrische Projektion zu ersetzen. Zum anderen wird die Unsicherheit neuronaler Netze implizit modelliert, indem eine Klassenhierarchie in den Trainingsprozess integriert wird. Insgesamt stellt diese Arbeit neuartige, performante Ansätze des 3D LiDAR-basierten, semantischen Szeneverstehens vor, welche zu einer Verbesserung der Leistung, Zuverlässigkeit und Sicherheit zukünftiger mobile Roboter und autonomer Fahrzeuge beitragen

Predictive Model of Driver\u27s Eye Fixation for Maneuver Prediction in the Design of Advanced Driving Assistance Systems

Over the last few years, Advanced Driver Assistance Systems (ADAS) have been shown to significantly reduce the number of vehicle accidents. Accord- ing to the National Highway Traffic Safety Administration (NHTSA), driver errors contribute to 94% of road collisions. This research aims to develop a predictive model of driver eye fixation by analyzing the driver eye and head information (cephalo-ocular) for maneuver prediction in an Advanced Driving Assistance System (ADAS). Several ADASs have been developed to help drivers to perform driving tasks in complex environments and many studies were conducted on improving automated systems. Some research has relied on the fact that the driver plays a crucial role in most driving scenarios, recognizing the driver’s role as the central element in ADASs. The way in which a driver monitors the surrounding environment is at least partially descriptive of the driver’s situation awareness. This thesis’s primary goal is the quantitative and qualitative analysis of driver behavior to determine the relationship between driver intent and actions. The RoadLab initiative provided an instrumented vehicle equipped with an on-board diagnostic system, an eye-gaze tracker, and a stereo vision system for the extraction of relevant features from the driver, the vehicle, and the environment. Several driver behavioral features are investigated to determine whether there is a relevant relation between the driver’s eye fixations and the prediction of driving maneuvers