RepSFNet : A Single Fusion Network with Structural Reparameterization for Crowd Counting

Crowd counting remains challenging in variable-density scenes due to scale variations, occlusions, and the high computational cost of existing models. To address this, we propose RepSFNet (Reparameterized Single Fusion Network), a lightweight architecture designed for accurate and real-time crowd estimation. RepSFNet combines large-kernel convolutional power with a efficient, suitable for low-power edge computing. The architecture includes three components: (i) a RepLK-ViT backbone using large reparameterized kernels for efficient multi-scale feature extraction; (ii) a Feature Fusion module that integrates ASPP and CAN for robust, density adaptive context modeling; and (iii) a Concatenate Fusion module to preserve spatial resolution and produce high-quality density maps. By avoiding attention mechanisms and multi-branch designs, RepSFNet reduces both parameters and FLOPs, enhancing runtime efficiency. The loss function combines Mean Squared Error (MSE) and Optimal Transport (OT), further improving count accuracy. Experiments on ShanghaiTech, NWPU, and UCF-QNRF show that RepSFNet delivers competitive accuracy with up to 34% lower inference latency compared to P2PNet, M-SFANet, M-SegNet, STEERER, and Gramformer, making it more efficient and suitable for low-power edge computing.