Alet (automated labeling of equipment and tools): A dataset for tool detection and human worker safety detection

Kurnaz, Fatih Can
For humans and robots to be able to collaborate in different tasks in the same real-life environments, robots need to be able to work with tools. This requires that they can recognize the tools, and identify their positions and orientations so that they can use them for their goals. However, neither robotics nor the computer vision community had a dataset to facilitate addressing these problems in real-life environments. In this study, we address these challenges and provide a dataset dedicated to detecting real-world tools in farming, gardening, office, stonemasonry, vehicle, workshop, and woodworking environments. Our dataset contains sophisticated environments and sometimes include humans using tools. These scenes also bear different challenges such as occlusion of tools, their inter-class invariance, and significant in-class variances. In addition, we form a baseline for our dataset using state-of-the-art object detection networks (including Faster R-CNN, Cascade R-CNN, RetinaNet, YOLOv3, RepPoint Detection, FreeAnchor, and Guided Anchor). We find that these object detectors have difficulty especially while detecting small scale tools. We also introduce synthetic images to our dataset with domain randomization and showed that they improve test results in our dataset. Moreover, as a side benefit of our dataset, we show that the annotations for the mask, helmet, headphone, glove, eye glasses tools allow us to train a novel deep network to detect whether safety measures have been taken by human workers. With these contributions, this study forms a basis for further research into tools and their use in computer vision and robotics applications.


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Citation Formats
F. C. Kurnaz, “Alet (automated labeling of equipment and tools): A dataset for tool detection and human worker safety detection,” M.S. - Master of Science, Middle East Technical University, 2020.