Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g. neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically study children behaviours. However, artificial models need to incorporate realistic sensory-motor information from the body to fully mimic the children's learning behaviours, e.g. the use of fingers to learn and manipulate numbers. To this end, this article presents a database of images, focused on number representation with fingers using both human and robot hands, which can constitute the base for building new realistic models of numerical cognition in humanoid robots, enabling a grounded learning approach in developmental autonomous agents. The article provides a benchmark analysis of the datasets in the database that are used to train, validate and test five state-of-the art deep neural networks, which are compared for classification accuracy together with an analysis of the computational requirements of each network. The discussion highlights the trade-off between speed and precision in the detection, which is required for realistic applications in robotics.

中文翻译：

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通过视觉手指识别在发展性神经机器人中学习数字的数据库。

数值认知是人类智力的基本组成部分，目前尚未完全理解。实际上，它是许多学科的研究主题，例如神经科学，教育，认知和发展心理学，数学哲学，语言学。在人工智能中，已经通过神经网络对数字认知的各个方面进行了建模，以复制和分析儿童的行为。但是，人工模型需要从身体吸收现实的感觉运动信息，以完全模仿儿童的学习行为，例如使用手指学习和操纵数字。为此，本文提供了一个图像数据库，重点是人和机器人手用手指进行数字表示，它可以构成在类人机器人中建立新的数字认知现实模型的基础，从而为发展中的自主主体提供扎实的学习方法。本文提供了对数据库中的数据集的基准分析，该数据集用于训练，验证和测试五个最先进的深度神经网络，将它们进行分类准确度比较，并对每个网络的计算需求进行分析。讨论强调了检测速度和精度之间的权衡，这是机器人技术中实际应用所必需的。验证和测试五个最先进的深度神经网络，将它们进行分类准确度比较，并对每个网络的计算需求进行分析。讨论强调了检测速度和精度之间的权衡，这是机器人技术中实际应用所必需的。验证和测试五个最先进的深度神经网络，将它们进行分类准确度比较，并对每个网络的计算需求进行分析。讨论强调了检测速度和精度之间的权衡，这是机器人技术中实际应用所必需的。