Detección de peatones en la noche usando Faster R-CNN e imágenes infrarrojas

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Michelle Alejandra Galarza Bravo https://orcid.org/0000-0001-8401-1871
Marco Flores http://orcid.org/0000-0001-7507-3325

Abstract

In this paper we present a system for pedestrian detection at nighttime conditions for vehicular safety applications. For this purpose, we analyze the Faster R-CNN performance for infrared images. So that we note that Faster R-CNN has problems to detect small scale pedestrians. For this reason, we present a new Faster R-CNN architecture focused on multi-scale detection, through two ROI’s generators for large size and small size pedestrians, RPNCD and RPNLD respectively. This architecture has been compared with the best Faster R-CNN baseline models, VGG-16 and Resnet 101, which present the best results. The experimental results have been development on CVC-09 and LSIFIR databases, which show improvements specially when detecting pedestrians that are far away, over the DET curve presents the miss rate versus FPPI of 16% and over the Precision vs Recall the AP of 89.85% for pedestrian class and the mAP of 90% over LSIFIR and CVC-09 test datasets.