Papers › Tiny Robotics Dataset and Benchmark for Continual Object Detection
Tiny Robotics Dataset and Benchmark for Continual Object Detection
Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze, Gian Antonio Susto, Nicola Bellotto
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.
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Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| TiROD | TiROD | YOLOv8n - KMeans Replay | Omega | 0.70 | #1 of 4 | Archive leaderboard | report |
| TiROD | TiROD | NanoDet Plus - KMeans Replay | Omega | 0.65 | #2 of 4 | Archive leaderboard | report |
| TiROD | TiROD | YOLOv8n - SID | Omega | 0.29 | #3 of 4 | Archive leaderboard | report |
| TiROD | TiROD | Nanodet Plus - SID | Omega | 0.27 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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