Fast Multi-scale fHOG Feature Extraction Using Histogram Downsampling

Polceanu, Mihai; Harrouet, Fabrice; Buche, Cédric

doi:10.1007/978-3-030-27544-0_5

Cited by 1 publication

(1 citation statement)

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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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Section: Related Workmentioning

confidence: 99%