Resumen

ESPINOSA-OVIEDO, Jorge Ernesto; VELASTIN, Sergio A.  y  BRANCH-BEDOYA, John William. EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios. Dyna rev.fac.nac.minas [online]. 2019, vol.86, n.211, pp.317-326. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v86n211.81639.

This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets.

Palabras clave : vehicle detection; motorcycle detection; Faster R-CNN; region-based detectors; convolutional neural network; deep learning.

        · resumen en Español     · texto en Inglés     · Inglés ( pdf )