EP-Net: An Equipment-Guided Dual-Stream CNN–Transformer Network for Fine-Grained Sports Image Classification

Abstract

Fine-grained sports recognition from static images is challenging because visually similar sports often exhibit nearly identical human poses, whereas their decisive differences are encoded by small-scale equipment and subtle human–equipment interactions. Existing single-stream convolutional or Transformer-based models tend to emphasize either local appearance or global context, making them vulnerable to equipment-detail loss and background interference. To address this problem, we propose the Equipment-Primed Network (EP-Net), a heterogeneous dual-stream architecture that treats sports equipment as a primary semantic cue for action discrimination. EP-Net employs an EfficientNetV2-S-based Equipment Stream to capture localized equipment shapes and textures and a Swin-Tiny-based Behavior Stream to model the athlete’s spatial configuration and global scene context. We further introduce a Cross-Modal Channel Attention (CMCA) module that projects equipment features into the behavior-feature space and performs directional channel recalibration. Unlike simple feature concatenation, CMCA uses equipment information to enhance action-relevant channels while reducing the relative influence of background-dominated responses. Experiments on the Sports-100 dataset show that EP-Net achieves a Top-1 accuracy of 98.80%, outperforming EfficientNetV2-S and Swin-Tiny by 3.00 and 2.35 percentage points, respectively. It also improves on naive dual-stream concatenation by 0.88 percentage points. Grad-CAM visualizations further indicate that EP-Net attends more consistently to discriminative equipment and human–equipment interaction regions. These results suggest that equipment-guided local–global feature interaction provides an effective solution to pose ambiguity and background interference in static fine-grained sports recognition.