Target coverage in camera networks for manufacturing workplaces

Hanoun, Samer; Bhatti, Asim; Creighton, Doug; Nahavandi, Saeid; Crothers, Phillip; Esparza, Celeste Gloria

doi:10.1007/s10845-014-0946-z

Cited by 19 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms that used heuristics, rather than random numbers, to select cameras were generally more efficient. Although PS, SA, and GA, which use random numbers, show superior performance compared to heuristics such as the greedy algorithm in the literature, those studies required few cameras and addressed smaller areas [35,36,38,[43][44][45]. Viz, 50 cameras signifies 2 50 or 1.1 × 10 15 different camera combinations and for 100 cameras becomes 1.2 × 10 30 .…”

Section: Results For Randomized Sitesmentioning

confidence: 99%

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

et al. 2020

View full text Add to dashboard Cite

Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.

show abstract

Section: Results For Randomized Sitesmentioning

confidence: 99%

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Literature [29] starts from the perspective of multivariety and small-batch production, adopts standard datasets to prove and solve the manufacturing scheduling of the flexible operation industry, considers the factors such as the maximum completion time, machine load rate, and total load rate as multiple scheduling objectives, and uses the method of analysis and deconstruction to verify the effectiveness and practicability of the algorithm. Literature [30] solves the flowshop problem of two machines, in which the optimization object is the minimum processing time and the dynamic programming algorithm is mainly used. Literature [31] conducted a detailed study on the flow line problem from three aspects of system modeling, solution method, and algorithm performance evaluation in 2020 and proposed the use of the Lagrangian algorithm to solve the static scheduling problem of flow line operations.…”

Section: Related Workmentioning

confidence: 99%

Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

Lan

Chen

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.

show abstract

“…The distance of a target point to a sensor is also considered to determine a perception of QoM in [16], but in visual networks, which means that the directional sensors are cameras. A QoM metric is defined and used to guide the network deployment based on a predefined set of discrete feasible configurations for all camera types.…”

Section: Related Workmentioning

confidence: 99%

“…It is important to distinguish between quality of image [8]- [11] and Quality of Monitoring (QoM) [12]- [16] in a visual network. The image quality assessment evaluates the image content degradation as consequence of acquisition, processing, compression, storage, transmission and reproduction processes.…”

Section: Introductionmentioning

confidence: 99%

FoV-Based Quality Assessment and Optimization for Area Coverage in Wireless Visual Sensor Networks

et al. 2020

View full text Add to dashboard Cite

Wireless visual sensor networks are commonly employed on several applications contexts such as smart cities, intelligent transportation systems and industrial management, aiming at the use of visual data from cameras to provide enhanced information and to expand the networks utilities. In these scenarios, some applications may require high-definition images when performing more specialized tasks, for example in face and text recognition, adding an important monitoring requirement when using camera-based sensors. In fact, it is important to ensure that the network is able to gather visual data with the associated required quality to each task, and such perceived quality may be processed as a function of the Field of View (FoV) of the visual sensors. In order to address this issue, new quality metrics are proposed for wireless visual sensor networks that are deployed to perform area coverage, exploiting for that different perceptions of the FoV. Those metrics are proposed along with redeployment optimization methods for visual sensor nodes aiming at the improvement of the perceived monitoring quality, which are based on greedy and evolutionary-based approaches. The proposed metrics and algorithms are expected to be more realistic than previous solutions, allowing flexible processing of variables as cameras' positions, orientations and viewing angles, providing then high flexibility on the definition of parameters and significantly contributing to the development of sensor networks based on visual sensors. INDEX TERMS Wireless sensor networks, Area coverage, Optimization, Visual quality, Quality of monitoring, Quality metric, Visual sensing, Field of view, Mathematical modelling.

show abstract

Target coverage in camera networks for manufacturing workplaces

Cited by 19 publications

References 43 publications

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

FoV-Based Quality Assessment and Optimization for Area Coverage in Wireless Visual Sensor Networks

Contact Info

Product

Resources

About