scholarly journals ENTODIG-3D

2019 ◽  
Vol 3 ◽  
Author(s):  
Toivo Ylinampa ◽  
Hannu Saarenmaa
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Optical Character Recognition ◽  
Point Cloud ◽  
3D Model ◽  
Two Dimensions ◽  
Learning Object ◽  
Qr Codes ◽  
Detection Program ◽  
Manual Handling

New innovations are needed to speed up digitisation of insect collections. More than one half of all specimens in scientific collections are pinned insects. In Europe this means 500-1,000 million such specimens. Today’s fastest mass-digitisation (i.e., imaging) systems for pinned insects can achieve circa 70 specimens/hour and 500/day by one operator (Tegelberg et al. 2014, Tegelberg et al. 2017). This is in contrast of the 5,000/day rate of the state-of-the-art mass-digitisation systems for herbarium sheets (Oever and Gofferje 2012). The slowness of imaging pinned insects follows from the fact that they are essentially 3D objects. Although butterflies, moths, dragonflies and similar large-winged insects can be prepared (spread) as 2D objects, the fact that the labels are pinned under the insect specimen makes even these samples 3D. In imaging, the labels are often removed manually, which slows down the imaging process. If the need for manual handling of the labels can be skipped, the imaging speed can easily multiplied. ENTODIG-3D (Fig. 1) is an automated camera system, which takes pictures of insect collection boxes (units and drawers) and digitizes them, minimizing time-consuming manual handling of specimens. “Units” are small boxes or trays contained in drawers of collection cabinets, and are being used in most major insect collections. A camera is mounted on motorized rails, which moves in two dimensions over a unit or a drawer. Camera movement is guided by a machine learning object detection program. QR-codes are printed and placed underneath the unit or drawer. QR-codes may contain additional information about each specimen, for example the place it originated from in the collection. Also, the object detection program detects the specimen, and stores its coordinates. The camera mount rotates and tilts, which ensures that the camera may take photographs from all angles and positions. Pictures are transferred into the computer, which calculates a 3D-model with photogrammetry, from which the label text beneath the specimen may be read. This approach requires heavy computation in the segmentation of the top images, and in the creation of a 3D model of the unit, and in extraction of label images of many specimens. Firstly, a sparse point cloud is calculated. Secondly, a dense point cloud is calculated. Finally, a textured mesh is calculated. With machine learning object detection, the top layer, which consists of the insect, may be removed. This leaves the bottom layer with labels visible for later processing by OCR (optical character recognition). This is a new approach to digitise pinned insects in collections. The physical setup is not expensive. Therefore, many systems could be installed in parallel to work overnight to produce the images of tens of drawers. The setup is not physically demanding for the specimens, as they can be left untouched in the unit or drawer. A digital object is created, consisting of label text, unit or drawer QR-code, specimen coordinates in a drawer with unique identifier, and a top-view photo of the specimen. The drawback of this approach is the heavy computing that is needed to create the 3D-models. ENTODIG-3D can currently digitise one sample in five minutes, almost without manual work. Theoretically, potentially sustainable rate is approximately one hundred thousand samples per year. The rate is similar as the current insect digitisation system in Helsinki (Tegelberg & al. 2017), but without the need for manual handling of individual specimens. By adding more computing power, the rate may be increased in linear fashion.

New Approach for Object Detection and Extraction from Digital Images for Providing a 3D Model Applicable in 3D GIS

2015 ◽  
Vol 4 (3) ◽  
pp. 34-58
Author(s):  
Amir Saeed Homainejad
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
3D Model ◽  
Aerial Image ◽  
City Management ◽  
Point Cloud Data ◽  
3D Gis ◽  
New Approach ◽  
Cloud Data ◽  
3D Space

This paper discusses a new approach in object extraction from aerial images with association of point cloud data. The extracted objects are captured in a 3D space for reconstructing a 3D model. The process includes three steps. In the first step the targeted objects are extracted from point cloud data and captured in a 3D space. The objects include buildings, trees, roads and background or terrain. In the second step the extracted objects are registered to the aerial image for assisting the object detection. Finally, the extracted objects from the aerial image are registered on the original 3D model for conversion to the point cloud data and then are captured in a 3D space for reconstructing a new 3D model. The final 3D model is flexible and editable. The objects can be edited, audited, and manipulated without affecting another objects or ruin the 3D model. Also, more data can be integrated in the 3D model improve its quality. The aspects of this project are: to reconstruct the final 3D model, and then each object can be interactively updated or modified without affecting the whole 3D model, and to provide a database for other users such as 3D GIS, city management and planning, Disaster Management System (DBS), and Smart City application.

New Approach for Object Detection and Extraction from Digital Images for Providing a 3D Model Applicable in 3D GIS

2019 ◽  
pp. 1324-1349
Author(s):  
Amir Saeed Homainejad
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
3D Model ◽  
Aerial Images ◽  
Aerial Image ◽  
Point Cloud Data ◽  
3D Gis ◽  
New Approach ◽  
Cloud Data ◽  
3D Space

This paper discusses a new approach in object extraction from aerial images with association of point cloud data. The extracted objects are captured in a 3D space for reconstructing a 3D model. The process includes three steps. In the first step the targeted objects are extracted from point cloud data and captured in a 3D space. The objects include buildings, trees, roads and background or terrain. In the second step the extracted objects are registered to the aerial image for assisting the object detection. Finally, the extracted objects from the aerial image are registered on the original 3D model for conversion to the point cloud data and then are captured in a 3D space for reconstructing a new 3D model. The final 3D model is flexible and editable. The objects can be edited, audited, and manipulated without affecting another objects or ruin the 3D model. Also, more data can be integrated in the 3D model improve its quality. The aspects of this project are: to reconstruct the final 3D model, and then each object can be interactively updated or modified without affecting the whole 3D model, and to provide a database for other users such as 3D GIS, city management and planning, Disaster Management System (DBS), and Smart City application.

scholarly journals Road Infrastructure Heritage: From Scan to InfraBIM

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Machine Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
3D Model ◽  
Machine Learning Techniques ◽  
Design Models ◽  
Learning Techniques ◽  
Historical Moment ◽  
The University ◽  
New Buildings

BIM is a methodology applied to the realization of design models applied to new buildings. To date,however, most of the building interventions as it happens in the field of cultural heritage are developed in theexisting. For this reason, scan to BIM procedures are improved and improved every day to make the use ofBIM easier. This document will describe the combination of different geomatics techniques used by theGeomatics Laboratory of the University of the Mediterranean in Reggio Calabria to create a BuildingInformation Model of a highway viaduct (infraBIM). In particular, we paid more attention to the scan to BIMphase through the segmentation of the point cloud using machine learning techniques that allow to obtain theconstitutive parametric elements of the 3D model. The model containing the geometric and physical data madeavailable by the ANAS management body in order to use the potential of infraBIM. This methodology today isof particular importance for the control, monitoring, intervention, and maintenance of road infrastructures,optimizing the procedures existing up to now. The advantages would be even more evident considering that weare living in a particular historical moment, in which a large number of bridges and viaducts in our nation aresubject to advanced forms of degradation

scholarly journals You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021 ◽  
Vol 11 (5) ◽  
pp. 2177
Author(s):  
Zuo Xiang ◽  
Patrick Seeling ◽  
Frank H. P. Fitzek
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Low Latency ◽  
Connected Vehicles ◽  
Network Nodes ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Broad Variety ◽  
Computational Resources ◽  
Service Latency

With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

Automated rebar diameter classification using point cloud data based machine learning

2021 ◽  
Vol 122 ◽  
pp. 103476
Author(s):  
Min-Koo Kim ◽  
Julian Pratama Putra Thedja ◽  
Hung-Lin Chi ◽  
Dong-Eun Lee
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

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