  Propelled by the rapid advancement of deep learning technologies, the YOLO
series has set a new benchmark for real-time object detectors. Researchers have
continuously explored innovative applications of reparameterization, efficient
layer aggregation networks, and anchor-free techniques on the foundation of
YOLO. To further enhance detection performance, Transformer-based structures
have been introduced, significantly expanding the model's receptive field and
achieving notable performance gains. However, such improvements come at a cost,
as the quadratic complexity of the self-attention mechanism increases the
computational burden of the model. Fortunately, the emergence of State Space
Models (SSM) as an innovative technology has effectively mitigated the issues
caused by quadratic complexity. In light of these advancements, we introduce
Mamba-YOLO a novel object detection model based on SSM. Mamba-YOLO not only
optimizes the SSM foundation but also adapts specifically for object detection
tasks. Given the potential limitations of SSM in sequence modeling, such as
insufficient receptive field and weak image locality, we have designed the
LSBlock and RGBlock. These modules enable more precise capture of local image
dependencies and significantly enhance the robustness of the model. Extensive
experimental results on the publicly available benchmark datasets COCO and VOC
demonstrate that Mamba-YOLO surpasses the existing YOLO series models in both
performance and competitiveness, showcasing its substantial potential and
competitive edge.The PyTorch code is available
at:\url{https://github.com/HZAI-ZJNU/Mamba-YOLO}
