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      Detección de peatones en el día y en la noche usando YOLO-v5 Translated title: Pedestrian detection at daytime and nighttime conditions based on YOLO-v5

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          Abstract

          Resumen En este artículo se presenta un nuevo algoritmo basado en aprendizaje profundo para la detección de peatones en el día y en la noche, denominada multiespectral, enfocado en aplicaciones de seguridad vehicular. La propuesta se basa en YOLO-v5, y consiste en la construcción de dos subredes que se enfocan en trabajar sobre las imágenes en color (RGB) y térmicas (IR), respectivamente. Luego se fusiona la información, a través, de una subred de fusión que integra las redes RGB e IR, para llegar a un detector de peatones. Los experimentos, destinados a verificar la calidad de la propuesta, fueron desarrollados usando distintas bases de datos públicas de peatones destinadas a su detección en el día y en la noche. Los principales resultados en función de la métrica mAP, estableciendo un IoU en 0.5 son 96.6 % sobre la base de datos INRIA, 89.2 % sobre CVC09, 90.5 % en LSIFIR, 56 % sobre FLIR-ADAS, 79.8 % para CVC14, 72.3 % sobre Nightowls y KAIST un 53.3 %

          Translated abstract

          Abstract This paper presents new algorithm based on deep learning for daytime and nighttime pedestrian detection, named multispectral, focused on vehicular safety applications. The proposal is based on YOLOv5, and consists of the construction of two subnetworks that focus on working with color (RGB) and thermal (IR) images, respectively. Then the information is merged, through a merging subnetwork that integrates RGB and IR networks to obtain a pedestrian detector. Experiments aimed at verifying the quality of the proposal were conducted using several public pedestrian databases for detecting pedestrians at daytime and nighttime. The main results according to the mAP metric, setting an IoU of 0.5 were: 96.6 % on the INRIA database, 89.2 % on CVC09, 90.5 % on LSIFIR, 56 % on FLIR-ADAS, 79.8 % on CVC14, 72.3 % on Nightowls and 53.3 % on KAIST.

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          Most cited references40

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          You Only Look Once: Unified, Real-Time Object Detection

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            The Pascal Visual Object Classes (VOC) Challenge

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              YOLOv3: An Incremental Improvement

              We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at https://pjreddie.com/yolo/ Tech Report
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                Author and article information

                Journal
                ing
                Ingenius. Revista de Ciencia y Tecnología
                Ingenius
                Universidad Politécnica Salesiana (Cuenca, Azuay, Ecuador )
                1390-650X
                1390-860X
                June 2022
                : 27
                : 85-95
                Affiliations
                [4] orgnameI&H Tech Ecuador mjflores@ 123456espe.edu.ec
                [3] orgnameI&H Tech Ecuador
                [1] orgnameUniversidad de las Fuerzas Armadas ESPE Ecuador
                [2] orgnameUniversidad de las Fuerzas Armadas ESPE Ecuador mjflores@ 123456espe.edu.ec
                Article
                S1390-860X2022000100085 S1390-860X(22)00002700085
                10.17163/ings.n27.2022.08
                88066b88-d707-432e-9e48-5ba0defbee39

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

                History
                : 13 May 2021
                : 13 September 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 40, Pages: 11
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                peatones,color,multispectral,pedestrian,aprendizaje profundo,YOLO-v5},infrarrojo,multiespectral,YOLO-v5,deep learning,Infrared

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