对比YOLO系列在机器人工作区目标检测中的表现，助力选型。
YOLO Object Detectors for Robotics -- a Comparative Study
- 用自定义数据集和COCO2017测试不同YOLO版本在机器人场景下的性能。
- 通过图像畸变验证各模型在噪声下的鲁棒性，结果覆盖多种训练/测试配置。
- 为机器人视觉任务提供具体模型选型参考，适合工业部署场景。
YOLO目标检测器已成为多个领域视觉系统的关键组件。该研究旨在验证YOLO系列模型在机器人工作区目标检测中的适用性。实验使用自定义数据集和COCO2017数据集，并对图像施加畸变以测试检测器的鲁棒性。研究涵盖多种训练/测试配置与模型版本，结果可为机器人视觉任务中合适YOLO版本的选择提供依据。
原文摘要 · Abstract (English)
YOLO object detectors recently became a key component of vision systems in many domains. The family of available YOLO models consists of multiple versions, each in various variants. The research reported in this paper aims to validate the applicability of members of this family to detect objects located within the robot workspace. In our experiments, we used our custom dataset and the COCO2017 dataset. To test the robustness of investigated detectors, the images of these datasets were subject to distortions. The results of our experiments, including variations of training/testing configurations and models, may support the choice of the appropriate YOLO version for robotic vision tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。