Football-YOLO: A Lightweight and Symmetry-Aware Football Detection Model with an Enlarged Receptive Field

In modern elite football, accurate ball localization is increasingly vital for smooth match flow and reliable officiating. Yet mainstream detectors still struggle with small objects like footballs in cluttered scenes due to limited receptive fields, weak feature representations, and non-trivial computational cost. To address these issues and introduce structural symmetry, we propose a lightweight framework that balances model complexity and representational completeness. Concretely, we design a Dynamic clustering C3k2 module (DcC3k2) to enlarge the effective receptive field and preserve local–global symmetry and a SegNeXt-based noise-attentive C3k2 module (SNAC3k2) to perform multi-scale suppression of background interference. For efficient feature extraction, we adopt GhostNetV2—a lightweight convolutional backbone—thereby maintaining computational symmetry and speed. Experiments on a Football dataset show that our approach improves mAP by 3.4% over strong baselines while reducing computation by 2.2%. These results validate symmetry-aware lightweight design as a promising direction for high-precision small-object detection in football analytics.