  This study explores the potential of super-resolution techniques in enhancing
object detection accuracy in football. Given the sport's fast-paced nature and
the critical importance of precise object (e.g. ball, player) tracking for both
analysis and broadcasting, super-resolution could offer significant
improvements. We investigate how advanced image processing through
super-resolution impacts the accuracy and reliability of object detection
algorithms in processing football match footage.
  Our methodology involved applying state-of-the-art super-resolution
techniques to a diverse set of football match videos from SoccerNet, followed
by object detection using Faster R-CNN. The performance of these algorithms,
both with and without super-resolution enhancement, was rigorously evaluated in
terms of detection accuracy.
  The results indicate a marked improvement in object detection accuracy when
super-resolution preprocessing is applied. The improvement of object detection
through the integration of super-resolution techniques yields significant
benefits, especially for low-resolution scenarios, with a notable 12\% increase
in mean Average Precision (mAP) at an IoU (Intersection over Union) range of
0.50:0.95 for 320x240 size images when increasing the resolution fourfold using
RLFN. As the dimensions increase, the magnitude of improvement becomes more
subdued; however, a discernible improvement in the quality of detection is
consistently evident. Additionally, we discuss the implications of these
findings for real-time sports analytics, player tracking, and the overall
viewing experience. The study contributes to the growing field of sports
technology by demonstrating the practical benefits and limitations of
integrating super-resolution techniques in football analytics and broadcasting.
