1,135 research outputs found
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and varying patient anatomies, reliable suture detection is
difficult, which is further complicated by occlusion and thread topologies. In
this paper, we propose a multi-stage framework for suture thread detection
based on deep learning. Fully convolutional neural networks are used to obtain
the initial detection and the overlapping status of suture thread, which are
later fused with the original image to learn a gradient road map of the thread.
Based on the gradient road map, multiple segments of the thread are extracted
and linked to form the whole thread using a curvilinear structure detector.
Experiments on two different types of sutures demonstrate the accuracy of the
proposed framework.Comment: Submitted to ICRA 201
Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping
The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability
Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices
Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data.
The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment
Automatic Rural Road Centerline Extraction from Aerial Images for a Forest Fire Support System
In the last decades, Portugal has been severely affected by forest fires which have caused
massive damage both environmentally and socially. Having a well-structured and precise
mapping of rural roads is critical to help firefighters to mitigate these events. The
traditional process of extracting rural roads centerlines from aerial images is extremely
time-consuming and tedious, because the mapping operator has to manually label the road
area and extract the road centerline.
A frequent challenge in the process of extracting rural roads centerlines is the high
amount of environmental complexity and road occlusions caused by vehicles, shadows, wild
vegetation, and trees, bringing heterogeneous segments that can be further improved. This
dissertation proposes an approach to automatically detect rural road segments as well as
extracting the road centerlines from aerial images.
The proposed method focuses on two main steps: on the first step, an architecture based
on a deep learning model (DeepLabV3+) is used, to extract the road features maps and
detect the rural roads. On the second step, the first stage of the process is an optimization
for improving road connections, as well as cleaning white small objects from the predicted
image by the neural network. Finally, a morphological approach is proposed to extract
the rural road centerlines from the previously detected roads by using thinning algorithms
like the Zhang-Suen and Guo-Hall methods.
With the automation of these two stages, it is now possible to detect and extract road
centerlines from complex rural environments automatically and faster than the traditional
ways, and possibly integrating that data in a Geographical Information System (GIS),
allowing the creation of real-time mapping applications.Nas últimas décadas, Portugal tem sido severamente afetado por fogos florestais, que têm
causado grandes estragos ambientais e sociais. Possuir um sistema de mapeamento de
estradas rurais bem estruturado e preciso é essencial para ajudar os bombeiros a mitigar
este tipo de eventos. Os processos tradicionais de extração de eixos de via em estradas
rurais a partir de imagens aéreas são extremamente demorados e fastidiosos. Um desafio
frequente na extração de eixos de via de estradas rurais é a alta complexidade dos ambientes
rurais e de estes serem obstruídos por veículos, sombras, vegetação selvagem e árvores,
trazendo segmentos heterogéneos que podem ser melhorados.
Esta dissertação propõe uma abordagem para detetar automaticamente estradas rurais,
bem como extrair os eixos de via de imagens aéreas.
O método proposto concentra-se em duas etapas principais: na primeira etapa é utilizada
uma arquitetura baseada em modelos de aprendizagem profunda (DeepLabV3+),
para detetar as estradas rurais. Na segunda etapa, primeiramente é proposta uma otimização
de intercessões melhorando as conexões relativas aos eixos de via, bem como a
remoção de pequenos artefactos que estejam a introduzir ruído nas imagens previstas pela
rede neuronal. E, por último, é utilizada uma abordagem morfológica para extrair os eixos
de via das estradas previamente detetadas recorrendo a algoritmos de esqueletização tais
como os algoritmos Zhang-Suen e Guo-Hall.
Automatizando estas etapas, é então possível extrair eixos de via de ambientes rurais
de grande complexidade de forma automática e com uma maior rapidez em relação aos
métodos tradicionais, permitindo, eventualmente, integrar os dados num Sistema de Informação
Geográfica (SIG), possibilitando a criação de aplicativos de mapeamento em tempo
real
Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications
It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects in its vicinity, which will fully guarantee the safety of the passengers and vehicle in various environment. 3D LIDAR can directly obtain the position and geometrical structure of the object within its detection range, while vision camera is very suitable for object recognition. Accordingly, this paper presents a novel object detection and identification method fusing the complementary information of two kind of sensors. We first utilize the 3D LIDAR data to generate accurate object-region proposals effectively. Then, these candidates are mapped into the image space where the regions of interest (ROI) of the proposals are selected and input to a convolutional neural network (CNN) for further object recognition. In order to identify all sizes of objects precisely, we combine the features of the last three layers of the CNN to extract multi-scale features of the ROIs. The evaluation results on the KITTI dataset demonstrate that : (1) Unlike sliding windows that produce thousands of candidate object-region proposals, 3D LIDAR provides an average of 86 real candidates per frame and the minimal recall rate is higher than 95%, which greatly lowers the proposals extraction time; (2) The average processing time for each frame of the proposed method is only 66.79ms, which meets the real-time demand of autonomous vehicles; (3) The average identification accuracies of our method for car and pedestrian on the moderate level are 89.04% and 78.18% respectively, which outperform most previous methods
Pavement crack detection and clustering via region-growing algorithm from 3D MLS point clouds
Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. FJC2018-035550-
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