MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26
arXiv:2607.12681
The paper introduces MambaPSA, a lightweight Mamba‑based module that replaces the C2PSA block in the YOLO26 object detector and adds a bidirectional Vision Mamba (BiViM) to the neck, achieving modest parameter and FLOP reductions while slightly improving CPU inference speed with minimal impact on detection accuracy.
Abstract
State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of Mamba into YOLO26, the latest non-maximum suppression (NMS)-free object detection framework, by proposing MambaPSA, a lightweight Mamba-based replacement for the C2PSA block at the end of the backbone. To complement this study, we additionally insert a bidirectional Vision Mamba (BiViM) module at the P3, P4, and P5 levels of the neck. Experiments on PASCAL VOC 2007+2012 show that MambaPSA reduces parameters by 2.9%, FLOPs by 12.1%, and improves CPU inference throughput by 17.6% (from 17 to 20 FPS) with negligible accuracy change (-0.1 mAP50:95), while the P4 BiViM placement yields the best accuracy gain (+0.9 mAP50:95). These results suggest that SSMs offer a favorable efficiency-accuracy trade-off when replacing attention-based blocks in NMS-free lightweight detectors.