Detecting objects in mobile robotics is crucial for numerous applications, from autonomous navigation to inspection. However, robots are often required to perform tasks in different domains with respect to the training one and need to adapt to these changes. Tiny mobile robots, subject to size, power, and computational constraints, encounter even more difficulties in running and adapting these algorithms. Such adaptability, though, is crucial for real-world deployment, where robots must operate effectively in dynamic and unpredictable settings. In this work, we introduce a novel benchmark to evaluate the continual learning capabilities of object detection systems in tiny robotic platforms. Our contributions include: (i) Tiny Robotics Object Detection (TiROD), a comprehensive dataset collected using a small mobile robot, designed to test the adaptability of object detectors across various domains and classes; (ii) an evaluation of state-of-the-art real-time object detectors combined with different continual learning strategies on this dataset, providing detailed insights into their performance and limitations; and (iii) we publish the data and the code to replicate the results to foster continuous advancements in this field. Our benchmark results indicate key challenges that must be addressed to advance the development of robust and efficient object detection systems for tiny robotics.

该研究解决了小型机器人在不同领域对象检测适应性不足的问题，提出了一个新的基准来评估目标检测系统的持续学习能力。研究中引入了Tiny Robotics Object Detection (TiROD)数据集，并对领先的实时目标检测算法结合不同的持续学习策略进行了评估，揭示了挑战和性能局限性，为小型机器人中的目标检测系统发展提供了重要指引。

持续目标检测的小型机器人数据集和基准