{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:24:20Z","timestamp":1770287060231,"version":"3.49.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T00:00:00Z","timestamp":1549411200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,5,14]]},"abstract":"<jats:p>\u00a0Object detection is a technologically challenging issue, which is useful for safety in outdoor environments, where this object, frequently, represents an obstacle that must be avoided. Although several object detection methods have been developed in recent years, they usually tend to produce poor results in outdoor environments, being mainly affected by sunlight, light intensity, shadows, and limited computational resources. This open problem is the main motivation for exploring the challenge of developing low-cost object detection solutions, with the characteristic of being easily adaptable and having low power requirements, such as the ones needed in on-board obstacle detection systems in automobiles. In this work, we present a trade-off analysis of several architectures using an FPGA-based design that implements ANNs (FPGA-ANN) for outdoor obstacle detection, focused in road safety. The analyzed FPGA-ANN architectures merge outdoor data gathered by a Kinect sensor, images and infrared data, to construct an outdoor environment model for object detection, which allows to detect if there is an obstacle in the near surroundings of a vehicle.<\/jats:p>","DOI":"10.3233\/jifs-169997","type":"journal-article","created":{"date-parts":[[2019,2,8]],"date-time":"2019-02-08T11:36:41Z","timestamp":1549625801000},"page":"4425-4436","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Real time FPGA-ANN architecture for outdoor obstacle detection focused in road safety"],"prefix":"10.1177","volume":"36","author":[{"given":"Ignacio","family":"Algredo-Badillo","sequence":"first","affiliation":[{"name":"Conacyt-Instituto Nacional de Astrofisica, Optica y Electronica, Luis Enrique Erro #1, Santa Maria Tonatzintla, Puebla, Mexico"}]},{"given":"Luis Alberto","family":"Morales-Rosales","sequence":"additional","affiliation":[{"name":"Conacyt-Universidad Michoacana de San Nicolas de Hidalgo, Gral. Francisco J. Mugica S\/N, Ciudad Universitaria, Morelia, Michoacan, Mexico"}]},{"given":"Carlos Arturo","family":"Hernandez-Gracidas","sequence":"additional","affiliation":[{"name":"Conacyt-Benemerita Universidad Autonoma de Puebla, 4 Sur #104; Col. Centro, Puebla de Zaragoza, Mexico"}]},{"given":"Juan Crescenciano","family":"Cruz-Victoria","sequence":"additional","affiliation":[{"name":"Universidad Politecnica de Tlaxcala, Avenida Universidad Politecnica No.1, San Pedro Xalcaltinco, Tlaxcala, Mexico"}]},{"given":"Daniel","family":"Pacheco-Bautista","sequence":"additional","affiliation":[{"name":"Universidad del Istmo, Ciudad Universitaria S\/N, Santa Cruz, Tehuantepec, Oaxaca, Mexico"}]},{"given":"Miguel","family":"Morales-Sandoval","sequence":"additional","affiliation":[{"name":"CINVESTAV, Carretera Victoria- Soto la Marina Kilometro 5.5, Ciudad Victoria - Soto la Marina, 87130 Cd Victoria, Tamps"}]}],"member":"179","published-online":{"date-parts":[[2019,2,6]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Oak Ridge National Laboratory Transportation Energy Data Book: Edition 33 ORNL-6990 Oak Ridge TN July 2014."},{"key":"e_1_3_1_3_2","article-title":"Global status report on road safety 2015","author":"World Health Organization","year":"2015","unstructured":"World Health Organization. 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