Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

A vision-based system for internal pipeline inspection

The internal inspection of large pipeline infrastructures, such as sewers and waterworks, is a fundamental task for the prevention of possible failures. In particular, visual inspection is typically performed by human operators on the basis of video sequences either acquired on-line or recorded for further off-line analysis. In this work, we propose a vision-based software approach to assist the human operator by conveniently showing the acquired data and by automatically detecting and highlighting the pipeline sections where relevant anomalies could occur

A Rover-based System for Searching Encrypted Targets in Unknown Environments

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MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method

MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method

A Practical Framework for the Development of Augmented Reality Applications by using ArUco Markers.

The Augmented Reality (AR) is an expanding field of the Computer Graphics (CG) that merges items of the real-world environment (e.g., places, objects) with digital information (e.g., multimedia files, virtual objects) to provide users with an enhanced interactive multi-sensorial experience of the real-world that surrounding them. Currently, a wide range of devices is used to vehicular AR systems. Common devices (e.g., cameras equipped on smartphones) enable users to receive multimedia information about target objects (non-immersive AR). Advanced devices (e.g., virtual windscreens) provide users with a set of virtual information about points of interest (POIs) or places (semi-immersive AR). Finally, an ever-increasing number of new devices (e.g., HeadMounted Display, HMD) support users to interact with mixed reality environments (immersive AR). This paper presents a practical framework for the development of non-immersive augmented reality applications through which target objects are enriched with multimedia information. On each target object is applied a different ArUco marker. When a specific application hosted inside a device recognizes, via camera, one of these markers, then the related multimedia information are loaded and added to the target object. The paper also reports a complete case study together with some considerations on the framework and future work