ImageTagger: An Open Source Online Platform for Collaborative Image Labeling

Fiedler, Niklas; Bestmann, Marc; Hendrich, Norman

doi:10.1007/978-3-030-27544-0_13

Cited by 36 publications

(26 citation statements)

References 6 publications

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“…One of the contributions of this study is our annotated dataset from the RoboCup 2019 competition using cameras mounted on the robots in both the controlled and natural lighting scenarios. We also used some images from previous competitions via the Image Tagger community-driven project [4]. Each of these raw images are manually annotated.…”

Section: A Datasetmentioning

confidence: 99%

xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

Barry

Shah

Keijsers

et al. 2019

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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With the emergence of onboard vision processing for areas such as the internet of things (IoT), edge computing and autonomous robots, there is increasing demand for computationally efficient convolutional neural network (CNN) models to perform real-time object detection on resource constraints hardware devices. Tiny-YOLO is generally considered as one of the faster object detectors for low-end devices and is the basis for our work. Our experiments on this network have shown that Tiny-YOLO can achieve 0.14 frames per second (FPS) on the Raspberry Pi 3 B, which is too slow for soccer playing autonomous humanoid robots detecting goal and ball objects. In this paper we propose an adaptation to the YOLO CNN model named xYOLO, that can achieve object detection at a speed of 9.66 FPS on the Raspberry Pi 3 B. This is achieved by trading an acceptable amount of accuracy, making the network approximately 70 times faster than Tiny-YOLO. Greater inference speed-ups were also achieved on a desktop CPU and GPU. Additionally we contribute an annotated Darknet dataset for goal and ball detection.

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Section: A Datasetmentioning

confidence: 99%

xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

Barry

Shah

Keijsers

et al. 2019

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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“…4. Some portion of the used dataset were taken from the ImageTagger library [16], which have annotated samples from different angles, cameras, and brightness. We extract the object coordinates by post-processing the blob-shaped network outputs.…”

Section: Visual Perceptionmentioning

confidence: 99%

NimbRo Robots Winning RoboCup 2018 Humanoid AdultSize Soccer Competitions

Farazi

Ficht

Allgeuer

et al. 2019

RoboCup 2018: Robot World Cup XXII

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Over the past few years, the Humanoid League rules have changed towards more realistic and challenging game environments, which encourage teams to advance their robot soccer performances. In this paper, we present the software and hardware designs that led our team NimbRo to win the competitions in the AdultSize league -including the soccer tournament, the drop-in games, and the technical challenges at RoboCup 2018 in Montréal. Altogether, this resulted in NimbRo winning the Best Humanoid Award. In particular, we describe our deep-learning approaches for visual perception and our new fully 3D printed robot NimbRo-OP2X.

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“…In total, the whole training process with around 3000 samples takes less than 40 minutes on a single Titan Black GPU with 6 GB memory. Some of the training samples were taken from the ImageTagger library [31] which have annotated samples from different angles, cameras, and brightness. Although the network produced very few falsepositives (around 1 % for the ball), we were able to reduce this value by utilising inference time augmentation.…”

Section: A Visual Perceptionmentioning

confidence: 99%

NimbRo-OP2X: Adult-Sized Open-Source 3D Printed Humanoid Robot

Ficht

Farazi

Brandenburger

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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Humanoid robotics research depends on capable robot platforms, but recently developed advanced platforms are often not available to other research groups, expensive, dangerous to operate, or closed-source. The lack of available platforms forces researchers to work with smaller robots, which have less strict dynamic constraints or with simulations, which lack many real-world effects. We developed NimbRo-OP2X to address this need. At a height of 135 cm our robot is large enough to interact in a human environment. Its low weight of only 19 kg makes the operation of the robot safe and easy, as no special operational equipment is necessary. Our robot is equipped with a fast onboard computer and a GPU to accelerate parallel computations. We extend our already opensource software by a deep-learning based vision system and gait parameter optimisation. The NimbRo-OP2X was evaluated during RoboCup 2018 in Montreál, Canada, where it won all possible awards in the Humanoid AdultSize class.

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ImageTagger: An Open Source Online Platform for Collaborative Image Labeling

Cited by 36 publications

References 6 publications

xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

NimbRo Robots Winning RoboCup 2018 Humanoid AdultSize Soccer Competitions

NimbRo-OP2X: Adult-Sized Open-Source 3D Printed Humanoid Robot

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