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

Real-time image mosaicking for mapping and exploration purposes

[Abstract] In the last decade, building mosaic images become an active field in several computer vision and graphic applications. In this paper, a panoramic image construction using monocular camera is proposed. In this approach, SURF algorithm is used to extract the keypoints in order to obtain reliable results for real-time applications. In addition, based on the homography between the panoramic and the new image, the rotation matrix is obtained, and the new image can be projected on a plane parallel to panorama. Finally, image illumination is compensated over the whole image and the calculation of the pixels contributed by each frame in the overlapping areas. The proposed approach has been verified with real flights, and the obtained results show the robustness of constructing panoramic image with minimal loosing in the information, furthermore, the results prove the ability of the proposed approach to create panoramic images in real-time applications.Ministerio de Economía, Industria y Competitividad; TRA2015-63708-RMinisterio de Economía y Competitividad; TRA2016-78886-C3-1-RComunidad de Madrid; S2013/MIT-271

Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks

In this paper, a novel approach for an automatic object detection and localisation on aerial images is proposed. Proposed model does not use ground control points (GCPs) and consists of three major phases. In the first phase, optimal flight route is planned in order to capture the area of interest and aerial images are acquired using unmanned aerial vehicle (UAV), followed by creating a mosaic of collected images to obtained larger field-of-view panoramic image of the area of interest and using the obtained image mosaic to create georeferenced map. The image mosaic is then also used to detect objects of interest using the approach based on convolutional neural networks

The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: a test of emerging integrated approaches at Cwm Idwal, North Wales

This paper was accepted for publication in the journal Geomorphology and the definitive published version is available at http://dx.doi.org/10.1016/j.geomorph.2014.07.021Novel topographic survey methods that integrate both structure-from-motion (SfM) photogrammetry and small unmanned aircraft systems (sUAS) are a rapidly evolving investigative technique. Due to the diverse range of survey configurations available and the infancy of these new methods, further research is required. Here, the accuracy, precision and potential applications of this approach are investigated. A total of 543 images of the Cwm Idwal moraine–mound complex were captured from a light (b5 kg) semi-autonomous multi-rotor unmanned aircraft system using a consumer-grade 18 MP compact digital camera. The imageswere used to produce a DSM(digital surfacemodel) of themoraines. The DSMis in good agreement with 7761 total station survey points providing a total verticalRMSE value of 0.517mand verticalRMSE values as lowas 0.200mfor less densely vegetated areas of the DSM. High-precision topographic data can be acquired rapidly using this technique with the resulting DSMs and orthorectified aerial imagery at sub-decimetre resolutions. Positional errors on the total station dataset, vegetation and steep terrain are identified as the causes of vertical disagreement. Whilst this aerial survey approach is advocated for use in a range of geomorphological settings, care must be taken to ensure that adequate ground control is applied to give a high degree of accuracy

Image Stitching for UAV remote sensing application

The objective of the project is to write an algorithm that is able to join top view images to create a big map. The project is done in the School of Castelldefels of UPC, within the research laboratory Icarus of EETAC Faculty. The goal of the project is to detect an area of this map, thanks to the analysis of this images. The images are taken by the two camera aboard on an Unmanned Aerial Vehicle (UAV) built by the Icarus group leaded by Enric Pastor. The implemented code is uploaded in Upc' svn at the adress: https://svn.fib.upc.es/svn/vincenzo.can

Automatic Image Stitching of Agriculture Areas based on Unmanned Aerial Vehicle using SURF

Identification  of  agricultural  areas  in  remote  sensing  technology  is  needed  for  the development  of  agricultural  areas.  The  image  of  the  agricultural  area  in  this  study  uses  an Unmanned Air Vehicle (UAV). The results of images taken from a height of 100 meters on the ground will be stored and processed into one image. UAV technology that supports this research is  expected  to  help  remote  sensing  in  real  time.  For  the  current  study,  measurements  in agricultural areas are related to some fragmented images. This article creates a beautiful view of the agricultural region. The author focuses on automatic image milling methods with detection- based image matching and description of patented local features from the dataset. The features method applied is based on speeded up robust features (SURF). The method of matching images and verification results is carried out. The result will create a 2-D spatial reference that starts the panorama size. This paper shows the results of image stitching in the agriculture area