Speed bumps and potholes are the most common road anomalies, significantly affecting ride comfort and vehicle stability. Preview-based suspension control mitigates their impact by detecting such irregularities in advance and adjusting suspension parameters proactively. Accurate and real-time detection is essential, but embedded deployment is constrained by limited computational resources and the small size of targets in input images.To address these challenges, this paper proposes SBP-YOLO, an efficient detection framework for speed bumps and potholes in embedded systems. Built upon YOLOv11n, it integrates GhostConv and VoVGSCSPC modules in the backbone and neck to reduce computation while enhancing multi-scale semantic features. A P2-level branch improves small-object detection, and a lightweight and efficient detection head (LEDH) maintains accuracy with minimal overhead. A hybrid training strategy further enhances robustness under varying road and environmental conditions, combining NWD loss, BCKD knowledge distillation, and Albumentations-based augmentation. Experiments show that SBP-YOLO achieves 87.0% mAP, outperforming the YOLOv11n baseline by 5.8%. After TensorRT FP16 quantization, it runs at 139.5 FPS on Jetson AGX Xavier, yielding a 12.4% speedup over the P2-enhanced YOLOv11. These results demonstrate the framework's suitability for fast, low-latency road condition perception in embedded suspension control systems.

翻译：减速带与坑洼是最常见的道路异常，显著影响乘坐舒适性与车辆稳定性。基于预瞄的悬架控制通过提前检测此类不规则物并主动调整悬架参数来减轻其影响。准确且实时的检测至关重要，但嵌入式部署受限于有限的计算资源以及输入图像中目标尺寸较小的问题。为应对这些挑战，本文提出SBP-YOLO，一种面向嵌入式系统的高效减速带与坑洼检测框架。该框架基于YOLOv11n构建，在骨干网络与颈部集成GhostConv与VoVGSCSPC模块，以减少计算量同时增强多尺度语义特征。一个P2级分支提升了小目标检测能力，而轻量高效的检测头（LEDH）以最小开销保持了精度。结合NWD损失、BCKD知识蒸馏及基于Albumentations的数据增强的混合训练策略，进一步增强了模型在不同道路与环境条件下的鲁棒性。实验表明，SBP-YOLO实现了87.0%的mAP，较YOLOv11n基线提升5.8%。经TensorRT FP16量化后，其在Jetson AGX Xavier上以139.5 FPS运行，相比P2增强的YOLOv11实现了12.4%的加速。这些结果证明了该框架适用于嵌入式悬架控制系统中快速、低延迟的路况感知任务。