Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints. A performance comparison of object detection methods, including both GPU and non-GPU implementations over a variety of on-road specific databases, is provided. Computer vision multi-class object detection can be integrated on sensor fusion modules where recall is preferred over precision. For this reason, ad hoc training with a single class for pedestrians has been performed and we achieved a significant increase in recall. Experiments have been carried out on several architectures and a special effort has been devoted to achieve a feasible computational time for a real-time system. Finally, an analysis of the input image size allows to fine-tune the model and get better results with practical costs.

中文翻译：

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使用通用单阶段算法和道路数据库进行实时车载行人检测

行人检测是物体检测的一种特殊情况，有助于减少高级驾驶员辅助系统和自动驾驶汽车中的事故。由于对象的可变性和时间限制，这不是一项容易的任务。提供了对象检测方法的性能比较，包括在各种道路特定数据库上的 GPU 和非 GPU 实现。计算机视觉多类目标检测可以集成到传感器融合模块上，其中召回优先于精度。出于这个原因，我们对行人进行了单一类别的临时训练，我们实现了召回率的显着提高。已经在多种架构上进行了实验，并致力于为实时系统实现可行的计算时间。最后，