  Object detection techniques that achieve state-of-the-art detection accuracy
employ convolutional neural networks, implemented to have optimal performance
in graphics processing units. Some hardware systems, such as mobile robots,
operate under constrained hardware situations, but still benefit from object
detection capabilities. Multiple network models have been proposed, achieving
comparable accuracy with reduced architectures and leaner operations. Motivated
by the need to create an object detection system for a soccer team of mobile
robots, this work provides a comparative study of recent proposals of neural
networks targeted towards constrained hardware environments, in the specific
task of soccer ball detection. We train multiple open implementations of
MobileNetV2 and MobileNetV3 models with different underlying architectures, as
well as YOLOv3, TinyYOLOv3, YOLOv4 and TinyYOLOv4 in an annotated image data
set captured using a mobile robot. We then report their mean average precision
on a test data set and their inference times in videos of different
resolutions, under constrained and unconstrained hardware configurations.
Results show that MobileNetV3 models have a good trade-off between mAP and
inference time in constrained scenarios only, while MobileNetV2 with high width
multipliers are appropriate for server-side inference. YOLO models in their
official implementations are not suitable for inference in CPUs.
