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Cited by 19Advanced Neural Network ApplicationsSmart Agriculture and AIDomain Adaptation and Few-Shot Learning

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

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Barry, Daniel, Shah, Munir, Keijsers, Merel, Khan, Humayun, Hopman, Banon·2019·2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)
DOI 10.1109/ivcnz48456.2019.8960963
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Abstract

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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  • xYOLO achieved greater inference speed on desktop CPU and GPU.

    “By using the normal convolutional layer and XNOR layer for training and recall, it achieved greater inference speed on desktop CPU and GPU.”

    From A survey of model compression strategies for object detection (2023) · cites this paper as [150]

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Related

Authors
Barry, D., Shah, M., Keijsers, M., Khan, H., & Hopman, B.
Affiliations
University of Canterbury (NZ)
ORCID
Venue
2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Publisher
IEEE
Published
2019-12
Pages
1-6
DOI
10.1109/ivcnz48456.2019.8960963
OpenAlex
W2980154845
Cited by
19
References
24
Citations by year
2026: 2 · 2025: 2 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 6 · 2020: 3
FWCI
0.81
Type
conference paper
Language
en
Open access
Licence
Locations
  • International Conference on Image and Vision Computing New Zealand (IVCNZ) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
Tiny-YOLO, real-time object detection, humanoid soccer robot, Raspberry Pi, edge computing, dark web, goal detection, ball detection, annotated dataset, inference acceleration, autonomous robots

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