Selective state-space modeling for real-time object detection in intelligent transportation: a systematic literature review

作者：Thanh Kiet Vo, René Jaros, Ly Duc Minh, P. Bilik, R. Martínek · 发表于：Artificial Intelligence Review · 年份：2026 · DOI：10.1007/s10462-026-11673-9

Real-time object detection is a cornerstone of intelligent transportation systems, where the YOLO (You Only Look Once) family has long defined the accuracy–speed trade-off. The emergence of Mamba—a selective state-space model that captures long-range dependencies in linear time—has triggered a wave of research embedding selective state spaces into YOLO detectors, promising global context without the quadratic cost of self-attention. Yet existing visual-Mamba surveys remain narrative and treat detection as one application among many; none isolates the YOLO-specific design space or assesses it under a reproducible protocol. Following the PRISMA-2020 methodology, this review screens 1053 Scopus records published between December 2023 and May 2026 and synthesizes 114 high-quality studies, 14 of them recovered through a documented two-pass re-evaluation procedure. We organize the field along a structured taxonomy of variant family, integration position, and application domain. The synthesis reveals pronounced terminology fragmentation—three-quarters of studies introduce custom Mamba variants—identifies YOLOv8, v11, and v5 as the dominant base frameworks, and shows that integration clusters at the feature-fusion, backbone, and attention positions. Critically, a Pareto analysis of 39 studies demonstrates that benchmark heterogeneity invalidates direct cross-study ranking, while medical imaging stays underexplored and only 33% of studies release code. The actionable implication for p...