轻量实时检测坑洼与减速带，助力智能悬架提前应对路况。
SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systems
- 基于YOLOv11n改进，融合轻量化模块提升多尺度特征
- 87.0% mAP，量化后达139.5 FPS，满足嵌入式实时需求
- 适合车载嵌入式系统部署，尤其适用于智能悬架感知
减速带和坑洼是常见道路异常，严重影响乘坐舒适性与车辆稳定性。基于预览的悬架控制通过提前检测这些异常并主动调整参数来缓解影响。准确且实时的检测至关重要，但嵌入式部署受限于计算资源不足及目标在图像中尺寸过小。为此，本文提出SBP-YOLO，一种面向嵌入式系统的轻量级实时检测框架。基于YOLOv11n，其在主干和颈部集成GhostConv与VoVGSCSPC模块以降低计算量，同时增强多尺度语义特征；引入P2级分支提升小目标检测能力；设计轻量高效检测头（LEDH），在保持精度的同时减少开销。采用混合训练策略，结合NWD损失、BCKD知识蒸馏与Albumentations增强，提升复杂路况下的鲁棒性。实验表明，SBP-YOLO达到87.0% mAP，较YOLOv11n基线提升5.8%。经TensorRT FP16量化后，在Jetson AGX Xavier上运行达139.5 FPS，较增强版YOLOv11提速12.4%。结果证明该框架适用于嵌入式悬架控制系统中的快速低延迟路况感知。
原文摘要 · Abstract (English)
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.
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