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Pioneering research for sustainable development

CITEEC carries out pioneering research in civil engineering, materials, sustainability and infrastructure. Thanks to multidisciplinary projects and international collaborations, scientific papers are being published that address key challenges facing the sector. This section presents the main papers and results produced by our research groups.

You can access a wide range of scientific publications, which can be filtered by subject, author, year and more. Use the filters provided to find relevant literature and learn more about each of our contributions to the advancement of knowledge.

Pioneering research for sustainable development

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2011

Rodriguez, Álvaro; Bermúdez, María; Rabuñal-Dopico, Juan R.; Puertas-Agudo, Jerónimo; Dorado, Julián; Pena-Mosquera, Luis; Balairón, Luis

Optical fish trajectory measurement in fishways through computer vision and artificial neural networks Journal Article

In: Journal of Computing in Civil Engineering, vol. 25, no. 4, pp. 291-301, 2011, ISSN: 08873801, (cited By 36).

Abstract | Links | BibTeX | Tags: Animal behavior; Biological variables; Camera systems; Computer vision techniques; Fish behavior; Fish management; Fish passage; Fish species; Hydrodynamic properties; Trajectory measurements; Upstream migration; Vertical-slot, Civil engineering; Computer applications; Computer networks; Computer vision; Fish; Fisheries; Fishways; Hydraulics; Systems analysis, Neural networks

@article{Rodriguez2011291,

title = {Optical fish trajectory measurement in fishways through computer vision and artificial neural networks},

author = {Álvaro Rodriguez and María Bermúdez and Juan R. Rabuñal-Dopico and Jerónimo Puertas-Agudo and Julián Dorado and Luis Pena-Mosquera and Luis Balairón},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960207630&doi=10.1061%2f%28ASCE%29CP.1943-5487.0000092&partnerID=40&md5=042dcd4e0017d3618043c98df392f627},

doi = {10.1061/(ASCE)CP.1943-5487.0000092},

issn = {08873801},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {Journal of Computing in Civil Engineering},

volume = {25},

number = {4},

pages = {291-301},

abstract = {Vertical slot fishways are hydraulic structures that allow the upstream migration of fish through obstructions in rivers. The appropriate design of a vertical slot fishway depends on the interplay between hydraulic and biological variables because the hydrodynamic properties of the fishway must match the requirements of the fish species for which it is intended. One of the primary difficulties associated with studies of real fish behavior in fishway models is that the existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper proposes a new procedure for measuring the behavior of the fish. The proposed technique uses artificial neural networks and computer vision techniques to analyze images obtained from the assays by means of a camera system designed for fishway integration. It is expected that this technique will provide detailed information about the fish behavior, and it will help to improve fish passage devices, which is currently a subject of interest in the area of civil engineering. A series of assays has been performed to validate this new approach in a full-scale fishway model with living fish. We have obtained very promising results that allow accurate reconstruction of the movements of the fish within the fishway. © 2011 American Society of Civil Engineers.},

note = {cited By 36},

keywords = {Animal behavior; Biological variables; Camera systems; Computer vision techniques; Fish behavior; Fish management; Fish passage; Fish species; Hydrodynamic properties; Trajectory measurements; Upstream migration; Vertical-slot, Civil engineering; Computer applications; Computer networks; Computer vision; Fish; Fisheries; Fishways; Hydraulics; Systems analysis, Neural networks},

pubstate = {published},

tppubtype = {article}

}