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Object Detection with Neural Models, Deep Learning and Common Sense to Aid Smart Mobility

机译：对象检测与神经模型，深度学习和常识辅助智能移动性

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摘要

The advent of autonomous transportation systems is attracting attention in AI today. Despite how far this area has progressed, there are situations better handled by humans. One of these is distinguishing objects seen for the first time and making decisions accordingly. Hence, our focus in this paper is on object detection, which can potentially enhance autonomous driving and other types of automation in transportation systems. This impacts Smart Mobility in Smart Cities. We provide expanded analysis of recent object detection techniques including neural models, deep learning and related advances. We highlight a novel object detection system called YOLO (You Only Look Once) and conduct its performance evaluation on real-time data. We point out challenges in this field and then explore the use of Commonsense Knowledge (CSK) in object detection with neural models and deep learning, emphasizing the importance of CSK to capture intuitive human reasoning. We explain how this work would potentially enhance autonomous vehicles and transportation systems. This work thus constitutes an exploratory paper that embodies a vision in Smart Mobility.
机译：自治运输系统的出现在今天的AI中引起了注意力。尽管这一领域进展了多远，但人类可以更好地处理情况。其中一个是区分第一次看到的物体并相应地进行决定。因此，我们对本文的重点是对象检测，这可能会潜在地提高运输系统中的自主驾驶和其他类型的自动化。这会影响智能城市的智能移动性。我们提供了对最近的对象检测技术的扩展分析，包括神经模型，深度学习和相关的进步。我们突出了一个名为YOLO（您只有一次）的新型对象检测系统，并对实时数据进行其性能评估。我们指出了这一领域的挑战，然后探讨了具有神经模型和深度学习的对象检测中的偶数知识（CSK），强调CSK捕捉直观的人类推理的重要性。我们解释了这项工作如何潜在地增强自治车辆和运输系统。因此，这项工作构成了一个体现了智能移动性愿景的探索性论文。

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