  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.
