A New Dataset and Performance Evaluation of a Region-Based CNN for Urban Object Detection

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Title: A New Dataset and Performance Evaluation of a Region-Based CNN for Urban Object Detection
Authors: Dominguez-Sanchez, Alex | Cazorla, Miguel | Orts-Escolano, Sergio
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Real-time object detection | Autonomous driving assistance system | Urban object detector | Convolutional neural networks
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 6-Nov-2018
Publisher: MDPI
Citation: Dominguez-Sanchez A, Cazorla M, Orts-Escolano S. A New Dataset and Performance Evaluation of a Region-Based CNN for Urban Object Detection. Electronics. 2018; 7(11):301. doi:10.3390/electronics7110301
Abstract: In recent years, we have seen a large growth in the number of applications which use deep learning-based object detectors. Autonomous driving assistance systems (ADAS) are one of the areas where they have the most impact. This work presents a novel study evaluating a state-of-the-art technique for urban object detection and localization. In particular, we investigated the performance of the Faster R-CNN method to detect and localize urban objects in a variety of outdoor urban videos involving pedestrians, cars, bicycles and other objects moving in the scene (urban driving). We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted on a vehicle. Furthermore, some of the data is weakly annotated so it can be used for testing weakly supervised learning techniques. There already exist urban object datasets, but none of them include all the essential urban objects. We carried out extensive experiments demonstrating the effectiveness of the baseline approach. Additionally, we propose an R-CNN plus tracking technique to accelerate the process of real-time urban object detection.
Sponsor: This work has been partially funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by the University of Alicante project GRE16-19.
URI: http://hdl.handle.net/10045/83811
ISSN: 2079-9292
DOI: 10.3390/electronics7110301
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/electronics7110301
Appears in Collections:INV - RoViT - Artículos de Revistas

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