Tiny Robotics Dataset and Benchmark for Continual Object Detection
This work addresses the need for adaptable object detection in resource-constrained tiny robotics, but it is incremental as it focuses on benchmarking existing methods on a new dataset.
The paper tackles the problem of adapting object detection for tiny mobile robots across different domains by introducing the TiROD dataset and benchmark, which evaluates continual learning strategies using NanoDet, highlighting key challenges in robustness and efficiency.
Detecting objects in mobile robotics is crucial for numerous applications, from autonomous navigation to inspection. However, robots often need to operate in different domains from those they were trained in, requiring them to adjust 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 the onboard camera of a small mobile robot, designed to test object detectors across various domains and classes; (ii) a benchmark of different continual learning strategies on this dataset using NanoDet, a lightweight object detector. Our results highlight key challenges in developing robust and efficient continual learning strategies for object detectors in tiny robotics.