AUTONOMOUS PHOTOGRAMMETRIC NETWORK DESIGN BASING ON CHANGING ENVIRONMENT GENETIC ALGORITHMS

In order to get good precision designer used to consider how to place cameras. Usually, design would refer to multidimensional optimal problem, so people used genetic algorithms to solve it. But genetic algorithms could result in premature or convergent problem. So sometime we get local minimum not global minimum and bring with vibrating phenomenon. Those problems will affect inaccurate design. So we try to solve them using the simulated annealing genetic algorithms. The methods give those species groups difference environment during difference stage to improve the property. Computer simulation result show that the way can accelerate the convergent speed and select good individual. This work would be used in other application. 1

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

3D Modeling of Objects by Using Resilient Neural Network

Camera Calibration (CC) is a fundamental issue for Shape-Capture, Robotic-Vision and 3D Reconstruction in Photogrammetry and Computer Vision. The purpose of CC is the determination of the intrinsic parameters of cameras for metric evaluation of the images. Classical CC methods comprise of taking images of objects with known geometry, extracting the features of the objects from the images, and minimizing their 3D backprojection errors. In this paper, a novel implicit-CC model (CC-RN) based on Resilient Neural Networks has been introduced. The CC-RN is particularly useful for 3D reconstruction of the applications that do not require explicitly computation of physical camera parameters in addition to the expert knowledge. The CC-RN supports intelligent-photogrammetry, photogrammetron. In order to evaluate the success of the proposed implicit-CC model, the 3D reconstruction performance of the CC-RN has been compared with two different well-known implementations of the Direct Linear Transformation (DLT). Extensive simulation results show that the CC-RN achieves a better performance than the well-known DLTs in the 3D backprojection of scene

Design and application of an automated system for camera photogrammetric calibration

This work presents the development of a novel Automatic Photogrammetric Camera Calibration System (APCCS) that is capable of calibrating cameras, regardless of their Field of View (FOV), resolution and sensitivity spectrum. Such calibrated cameras can, despite lens distortion, accurately determine vectors in a desired reference frame for any image coordinate, and map points in the reference frame to their corresponding image coordinates. The proposed system is based on a robotic arm which presents an interchangeable light source to the camera in a sequence of known discrete poses. A computer captures the camera's image for each robot pose and locates the light source centre in the image for each point in the sequence. Careful selection of the robot poses allows cost functions dependant on the captured poses and light source centres to be formulated for each of the desired calibration parameters. These parameters are the Brown model parameters to convert from the distorted to the undistorted image (and vice versa), the focal length, and the camera's pose. The pose is split into the camera pose relative to its mount and the mount's pose relative to the reference frame to aid subsequent camera replacement. The parameters that minimise each cost function are deter- mined via a combination of coarse global and fine local optimisation techniques: genetic algorithms and the Leapfrog algorithm, respectively. The real world applicability of the APCCS is assessed by photogrammetrically stitching cameras of differing resolutions, FOVs and spectra into a single multi- spectral panorama. The quality of these panoramas are deemed acceptable after both subjective and quantitative analyses. The quantitative analysis compares the stitched position of matched image feature pairs found with the Shape Invariant Feature Tracker (SIFT) and Speeded Up Robust Features (SURF) algorithms and shows the stitching to be accurate to within 0.3°. The noise sensitivity of the APCCS is assessed via the generation of synthetic light source centres and robot poses. The data is realistically created for a hy- pothetical camera pair via the corruption of ideal data using seven noise sources emulating the robot movement, camera mounting and image processing errors. The calibration and resulting stitching accuracies are shown to be largely independent of the noise magnitudes in the operational ranges tested. The APCCS is thus found to be robust to noise. The APCCS is shown to meet all its requirements by determining a novel combination of calibration parameters for cameras regardless of their properties in a noise resilient manner

A wireless sensor network-based approach to large-scale dimensional metrology

In many branches of industry, dimensional measurements have become an important part of the production cycle, in order to check product compliance with specifications. This task is not trivial especially when dealing with largescale dimensional measurements: the bigger the measurement dimensions are, the harder is to achieve high accuracies. Nowadays, the problem can be handled using many metrological systems, based on different technologies (e.g. optical, mechanical, electromagnetic). Each of these systems is more or less adequate, depending upon measuring conditions, user's experience and skill, or other factors such as time, cost, accuracy and portability. This article focuses on a new possible approach to large-scale dimensional metrology based on wireless sensor networks. Advantages and drawbacks of such approach are analysed and deeply discussed. Then, the article briefly presents a recent prototype system - the Mobile Spatial Coordinate-Measuring System (MScMS-II) - which has been developed at the Industrial Metrology and Quality Laboratory of DISPEA - Politecnico di Torino. The system seems to be suitable for performing dimensional measurements of large-size objects (sizes on the order of several meters). Owing to its distributed nature, the system - based on a wireless network of optical devices - is portable, fully scalable with respect to dimensions and shapes and easily adaptable to different working environments. Preliminary results of experimental tests, aimed at evaluating system performance as well as research perspectives for further improvements, are discusse

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version

Accuracy Evaluation of Dense Matching Techniques for Casting Part Dimensional Verification

Product optimization for casting and post-casting manufacturing processes is becoming compulsory to compete in the current global manufacturing scenario. Casting design, simulation and verification tools are becoming crucial for eliminating oversized dimensions without affecting the casting component functionality. Thus, material and production costs decrease to maintain the foundry process profitable on the large-scale component supplier market. New measurement methods, such as dense matching techniques, rely on surface texture of casting parts to enable the 3D dense reconstruction of surface points without the need of an active light source as usually applied with 3D scanning optical sensors. This paper presents the accuracy evaluation of dense matching based approaches for casting part verification. It compares the accuracy obtained by dense matching technique with already certified and validated optical measuring methods. This uncertainty evaluation exercise considers both artificial targets and key natural points to quantify the possibilities and scope of each approximation. Obtained results, for both lab and workshop conditions, show that this image data processing procedure is fit for purpose to fulfill the required measurement tolerances for casting part manufacturing processes.This research was partially funded by ESTRATEUS project (Reference IE14-396). given are accurate and use the standard spelling of funding agency names at https://search.crossref.org/funding, any errors may affect your future funding

Optimisation of camera positions for optical coordinate measurement based on visible point analysis

In optical coordinate measurement using cameras, the number of images, and positions and orientations of the cameras, are critical to object accessibility and the accuracy of a measurement. In this paper, we propose a technique to optimise the number of cameras and the positions of these cameras for the measurement of a given object using visible point analysis of the object's computer aided design data. The visible point analysis technique is based on a hidden point removal approach; this technique is used to detect which surface points on the object are visible from a given camera position. A genetic algorithm is used to find the set of positions that provide optimum surface point density and overlap between views, while minimising the total number of camera images required. The genetic algorithm is used to minimise the measurement data processing time while maintaining optimum surface point density. We test this optimisation procedure on four artefacts and the measurements are shown to be comparable to that from a traceable contact co-ordinate measurement machine. We show that using our procedure improves the measurement quality compared to the more conventional approach of using equally spaced images. This work is part of a larger effort to fully automate and optimise optical coordinate measurement techniques

Improving Tree Crown Mapping using Airborne LiDAR with Genetic Algorithms

Landscape-scale mapping of individual trees derived from LiDAR (Light Detection And Ranging) data have been found to be valuable for a wide range of environmental analyses including carbon inventories; fuel estimations for wildfire risk assessment and management. These mapping efforts use individual tree crown (ITC) recognition algorithms applied to LiDAR point clouds, which have complex parameter sets. Genetic algorithms (GA) have been demonstrated to be excellent function optimizers for very complex search spaces and perform well for parameter tuning. Here, we use GAs to identify the best of a set of published ITC models and their optimal parameters for airborne LiDAR of forested plots in the Sierra Nevada Mountains of California. We assessed the accuracy of these ITC models in terms of the F-score and percentage bias for tree crown prediction. GA-optimization generally improved on ITC default parameters and showed that these models typically perform better for detecting overstory trees