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首页> 外文会议>International Conference on Information and Computer Technologies >Obstacle Detection Using Faster R-CNN Oriented to an Autonomous Feeding Assistance System
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Obstacle Detection Using Faster R-CNN Oriented to an Autonomous Feeding Assistance System

机译：使用面向自主进食辅助系统的更快R-CNN进行障碍物检测

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Obstacle detection has been a relevant issue for the implementation of autonomous robotic systems, within which increasingly robust algorithms have begun to be applied, especially Deep Learning techniques. However, these have not been widely used for the detection of obstacles in static robotic agents, contrary to what happens with mobile agents. For this reason, this work explores the use of one of these techniques, which is a neural network based on the Faster R-CNN, focused on detecting a specific obstacle (hands) in an application environment for a food assistance robot. For this purpose, a database containing 6205 training images and 1350 validation images was prepared, where 31 users perform different movements with their hands. To verify the capacity of the network, 3 architectures of different depths were implemented, which were evaluated and compared, resulting in the network of greater depth obtained the highest accuracy, of 77.4%, taking into account that the hands are not only still but also in movement, generating distortion in them and greater difficulty for their detection. Also, the internal behavior of the network was visualized through activations, to verify what it had learned, showing that it managed to focus on the hands, with some activations located in parts of the user's body such as face and arm.
机译：障碍检测已成为实现自主机器人系统的一个相关问题，在该系统中，已经开始应用越来越强大的算法，尤其是深度学习技术。但是，与移动代理程序所发生的情况相反，这些方法尚未广泛用于检测静态机器人代理程序中的障碍。因此，这项工作探索了其中一种技术的使用，该技术是基于Faster R-CNN的神经网络，专注于在食品辅助机器人的应用环境中检测特定的障碍物（手）。为此，准备了一个包含6205个训练图像和1350个验证图像的数据库，其中31位用户用手进行了不同的动作。为了验证网络的容量，实施了3种不同深度的体系结构，并对其进行了评估和比较，结果是，考虑到手不仅静止不动而且还可以动手，深度更大的网络获得了77.4％的最高精度。在运动中会产生扭曲，并增加检测难度。同样，通过激活将网络的内部行为可视化，以验证其学到的知识，表明它设法将注意力集中在手上，而一些激活位于用户身体的某些部位，例如面部和手臂。

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