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

Download

12625631.pdf

Date

2020-8

Author

Kurnaz, Fatih Can

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

223
views

354
downloads

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.

Subject Keywords

Object Detection, Tool Dataset, Safety Detection

URI

https://hdl.handle.net/11511/69288

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Lifelong learning and personalization in long-term human-robot interaction (LEAP-HRI) Irfan, Bahar; Ramachandran, Aditi; Spaulding, Samuel; Kalkan, Sinan; Parisi, German I.; Gunes, Hatice (2021-03-08) While most of the research in Human-Robot Interaction (HRI) focuses on short-term interactions, long-term interactions require bolder developments and a substantial amount of resources, especially if the robots are deployed in the wild. Robots need to incrementally learn new concepts or abilities in a lifelong fashion to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The "Lifelong Learning and Personalization in Long-Term ...
Cooperative Pollution Source Exploration and Cleanup with a Bio-inspired Swarm Robot Aggregation Sadeghi Amjadi, Arash; Raoufi, Mohsen; Turgut, Ali Emre; Broughton, George; Krajník, Tomáš; Arvin, Farshad (2021-01-01) Using robots for exploration of extreme and hazardous environments has the potential to significantly improve human safety. For example, robotic solutions can be deployed to find the source of a chemical leakage and clean the contaminated area. This paper demonstrates a proof-of-concept bio-inspired exploration method using a swarm robotic system based on a combination of two bio-inspired behaviors: aggregation, and pheromone tracking. The main idea of the work presented is to follow pheromone trails to fin...
Designing Social Cues for Collaborative Robots: The Role of Gaze and Breathing in Human-Robot Collaboration Terzioglu, Yunus; Mutlu, Bilge; Şahin, Erol (2020-01-01) In this paper, we investigate how collaborative robots, or cobots, typically composed of a robotic arm and a gripper carrying out manipulation tasks alongside human coworkers, can be enhanced with HRI capabilities by applying ideas and principles from character animation. To this end, we modified the appearance and behaviors of a cobot, with minimal impact on its functionality and performance, and studied the extent to which these modifications improved its communication with and perceptions by human collab...
Hierarchical incremental context modeling on robots Doğan, Fethiye Irmak; Kalkan, Sinan; Department of Computer Engineering (2017) Context is very crucial for robots to be able to adapt themselves to circumstances and to fulfill their tasks accordingly. There have been many studies on modeling context on robots, however, these studies either do not construct an incremental and hierarchical structure (i.e., use a fixed number of contexts and context layers) or determine the necessity of adding a new context by using rule-based approaches. In this thesis, we propose two different methods to model context. In the first method, we extend t...
Knowledge-sharing techniques for Egocentric Navigation KESKİNPALA, Türker; Koku, Ahmet Buğra; WİLKES, D Mitch; KAWAMURA, kazuhiko (2003-10-08) Teams of humans and robots working together can provide effective solutions to problems. In such applications, effective human-robot teaming relies on being able to communicate information about the current perception or understanding of the environment. In this paper, human-robot teaming on navigational tasks is discussed The role of the human user will be to specify the goal point(s) for the robot, and also to interact with the robot in the event of perceptual errors. A novel navigation method, Egocentric...

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.