Artículo
On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data
Autor/es | Carranza García, Manuel
Torres Mateo, Jesús Lara Benítez, Pedro García Gutiérrez, Jorge |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2021 |
Fecha de depósito | 2022-02-18 |
Publicado en |
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Resumen | Object detection using remote sensing data is a key task of the perception systems of
self-driving vehicles. While many generic deep learning architectures have been proposed for this
problem, there is little guidance ... Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use theWaymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España Junta de Andalucía |
Identificador del proyecto | TIN2017-88209-C2-2-R
US-1263341 P18-RT-2778 |
Cita | Carranza García, M., Torres Mateo, J., Lara Benítez, P. y García Gutiérrez, J. (2021). On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data. Remote Sensing, 13 (1) |
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