Tiny Robotics Dataset and Benchmark for Continual Object Detection

• URL: http://arxiv.org/abs/2409.16215v1
• Date: Tue, 24 Sep 2024 16:21:27 GMT
• Title: Tiny Robotics Dataset and Benchmark for Continual Object Detection
• Authors: Francesco Pasti, Riccardo De Monte, Davide Dalle Pezze, Gian Antonio Susto, Nicola Bellotto,
• Abstract summary: This work introduces 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; and (iii) we publish the data and the code to replicate the results to foster continuous advancements in this field.
• Score: 6.4036245876073234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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