Hong Qing, Dong Hao, Deng Wei, Ping Yihan
School of Mechanical Engineering, Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu, China.
Department of Economic Management, Shandong Vocational College of Science and Technology, Weifang, Shandong, China.
Front Neurorobot. 2024 Jan 4;17:1338104. doi: 10.3389/fnbot.2023.1338104. eCollection 2023.
The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment of students, identify targets, and interact with them. Object detection in complex environments remains challenging, as classrooms or learning scenarios involve various objects, backgrounds, and lighting conditions. Improving the accuracy and efficiency of object detection is crucial for the development of education robots. This paper introduces the progress of an education robot's object detection based on a brain-inspired heuristic method, which integrates Faster R-CNN, YOLOv3, and semi-supervised learning. By combining the strengths of these three techniques, we can improve the accuracy and efficiency of object detection in education robot systems. In this work, we integrate two popular object detection algorithms: Faster R-CNN and YOLOv3. We conduct a series of experiments on the task of education robot object detection. The experimental results demonstrate that our proposed optimization algorithm significantly outperforms individual algorithms in terms of accuracy and real-time performance. Moreover, through semi-supervised learning, we achieve better performance with fewer labeled samples. This will provide education robots with more accurate perception capabilities, enabling better interaction with students and delivering personalized educational experiences. It will drive the development of the field of education robots, offering innovative and personalized solutions for education.
教育机器人的发展给教育领域带来了巨大的潜力和机遇。这些智能机器可以在教室和学习环境中与学生互动，提供个性化的教育支持。为了使教育机器人能够发挥其作用，它们需要精确的目标检测能力，以便感知和理解学生周围的环境、识别目标并与目标进行互动。在复杂环境中进行目标检测仍然具有挑战性，因为教室或学习场景涉及各种物体、背景和光照条件。提高目标检测的准确性和效率对于教育机器人的发展至关重要。本文介绍了一种基于受大脑启发的启发式方法的教育机器人目标检测进展，该方法集成了Faster R-CNN、YOLOv3和半监督学习。通过结合这三种技术的优势，我们可以提高教育机器人系统中目标检测的准确性和效率。在这项工作中，我们集成了两种流行的目标检测算法：Faster R-CNN和YOLOv3。我们针对教育机器人目标检测任务进行了一系列实验。实验结果表明，我们提出的优化算法在准确性和实时性能方面显著优于单个算法。此外，通过半监督学习，我们用更少的标记样本实现了更好的性能。这将为教育机器人提供更准确的感知能力，使其能够更好地与学生互动并提供个性化的教育体验。它将推动教育机器人领域的发展，为教育提供创新和个性化的解决方案。
