Fast and Precise Black and White Ball Detection for RoboCup Soccer

Menashe, Jacob; Kelle, Josh; Genter, Katie; Hanna, Josiah P.; Liebman, Elad; Narvekar, Sanmit; Zhang, Ruohan; Stone, Peter

doi:10.1007/978-3-030-00308-1_4

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…The vision pipelines used by the competing teams have changed in tandem, going from human-engineered vision methods [31,32] to pipelines relying increasingly on machine learning. Several teams have used convolutional neural networks either for binary classification tasks [7,8] or to detect several relevant object categories [9,10]. These methods, however, use CNNs for classification only, therefore they still require a separate object proposal method, and the quality of the system may largely depend on the efficiency of the algorithm used to generate candidates for classification.…”

Section: Computer Vision In Robocupmentioning

confidence: 99%

“…Due to the rapidly increasing power of hardware, the usually computationally expensive networks have started to appear in mobile and embedded systems [6]. Several teams [7][8][9][10][11] have used convolutional neural networks in the 2017 SPL league in RoboCup to classify relevant objects on the soccer field. However, due to the limitations of the robot's hardware, these networks were relatively shallow and were designed to classify fixed-resolution image patches only.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL

Szemenyei

Estivill‐Castro

2019

RoboCup 2018: Robot World Cup XXII

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Convolutional neural networks (CNNs) are the state-of-the-art method for most computer vision tasks. However, their excessive computational requirements make the deployment of CNNs on mobile or embedded platforms challenging. In this paper, we present and end-to-end neural network solution to scene understanding in the context of robot soccer. Our system uses two key neural networks: one to perform semantic segmentation on an image, and another to propagate class labels between consecutive frames. Our networks are trained on synthetic datasets and fine-tuned on a set consisting of real images taken using a Nao robot. Furthermore, we discuss and evaluate several practical methods for increasing the efficiency and performance of our networks. Finally, we present NaoDNN, a C++ neural network library designed for fast inference on the Nao robots.

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Section: Computer Vision In Robocupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL

Szemenyei

Estivill‐Castro

2019

RoboCup 2018: Robot World Cup XXII

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“…Traditionally, in the context of the RoboCup humanoid robotics competition, colour segmentation based techniques have been used to detect features of the soccer field, such as goals and balls [16] [17]. These techniques are fast and can achieve good accuracy in simplistic environments, for example the use of an orange ball, controlled indoor lighting and yellow coloured goals.…”

Section: Related Workmentioning

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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“…The training time of this classifier was about 10 hours that is an issue for on-site training during the competitions. Recently several neural network based classifiers are presented for classic ball detection in standard platform league [11][12][13]. In [11] ball candidates are examined using black pentagons of the ball.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Ball Detection Approach Using Convolutional Neural Networks

Teimouri

Delavaran

Rezaei

2019

RoboCup 2019: Robot World Cup XXIII

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Ball detection is one of the most important tasks in the context of soccer-playing robots. The ball is a small moving object which can be blurred and occluded in many situations. Several neural network based methods with different architectures are proposed to deal with the ball detection. However, they are either neglecting to consider the computationally low resources of humanoid robots or highly depend on manually-tuned heuristic methods to extract the ball candidates. In this paper, we propose a new ball detection method for low-cost humanoid robots that can detect most soccer balls with a high accuracy rate of up to 97.17%. The proposed method is divided into two steps. First, some coarse regions that may contain a full ball are extracted using an iterative method employing an efficient integral image based feature. Then they are fed to a lightweight convolutional neural network to finalize the bounding box of a ball. We have evaluated the proposed approach using a comprehensive dataset and the experimental results show the efficiency of our method.

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Fast and Precise Black and White Ball Detection for RoboCup Soccer

Cited by 19 publications

References 6 publications

Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL

Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL

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

A Real-Time Ball Detection Approach Using Convolutional Neural Networks

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