Improving Object Detection Quality in Football Through Super-Resolution Techniques

Karolina Seweryn, Gabriel Chec, Szymon Lukasik, Anna Wróblewska

Abstract: This research examines the effectiveness of super-resolution techniques in improving object detection accuracy in football. Given the sport’s fast pace and the need for precise tracking of players and the ball, super-resolution can offer significant improvements. The study applies super-resolution techniques to SoccerNet football videos and evaluates their impact on Faster R-CNN detection accuracy. Findings reveal a significant boost in object detection accuracy following the application of super-resolution preprocessing. Enhancing object detection by integrating super-resolution techniques provides substantial advantages, particularly in low-resolution settings, with a 12% rise in mean Average Precision (mAP) at an IoU (Intersection over Union) range of 0.50:0.95 for 320 \(\times \) 240 pixel images when the resolution is quadrupled using RLFN. As the image dimensions grow, the extent of improvement becomes less pronounced; however, a consistent enhancement in detection quality remains clear. Moreover, the implications of these results for real-time sports analytics, player tracking, and the overall viewing experience are discussed.

External IDs:dblp:conf/iccS/SewerynCLW25