Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios

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.


page 6

page 12


YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

Object detection remains an active area of research in the field of comp...

A Comprehensive Study on Object Detection Techniques in Unconstrained Environments

Object detection is a crucial task in computer vision that aims to ident...

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

Inverted bottleneck layers, which are built upon depthwise convolutions,...

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

With the emergence of onboard vision processing for areas such as the in...

Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)

Object detection in thermal infrared spectrum provides more reliable dat...

ROBO: Robust, Fully Neural Object Detection for Robot Soccer

Deep Learning has become exceptionally popular in the last few years due...

POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors

For vehicle autonomy, driver assistance and situational awareness, it is...

Please sign up or login with your details

Forgot password? Click here to reset