数字认知是人类智力的基本组成部分,尚未完全理解。的确,它是许多学科的研究课题,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,通过神经网络对数字认知的各个方面进行建模,以复制和分析研究儿童的行为。然而,人工模型需要结合来自身体的真实感觉运动信息,以完全模仿儿童的学习行为,例如,使用手指来学习和操纵数字。为此,本文介绍了一个图像数据库,专注于使用人类和机器人手的手指数字表示,这可以构成在人形机器人中建立新的现实数字认知模型的基础,在发展性自主代理中实现扎根的学习方法。文章对数据库中用于训练的数据集进行了基准分析,验证,测试五个最先进的深度神经网络,比较分类精度,并分析每个网络的计算要求。讨论强调了检测中速度和精度之间的权衡,这是机器人实际应用所必需的。
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.