标题： SBP-YOLO：一种轻量级实时模型用于检测减速带和坑洼 显示英文标题

标题： SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes

摘要： 可靠且实时地检测道路减速带和坑洼对于高级悬挂系统中的前瞻性感知至关重要，能够实现及时和自适应的阻尼控制。 由于计算资源有限以及远距离目标规模较小，在嵌入式平台上实现高精度和高效率仍然具有挑战性。 本文提出了SBP-YOLO，这是一种轻量级且高速的检测框架，专门用于减速带和坑洼识别。 基于YOLOv11n，该模型在主干网络和颈部集成GhostConv和VoVGSCSPC模块，以减少计算量同时增强多尺度语义特征。 为了提高小目标检测，引入了一个P2级分支，并采用轻量且高效的检测头LEDH，从而减轻增加的计算开销而不影响准确性。 一种结合NWD损失、主干级知识蒸馏和Albumentations驱动增强的混合训练策略进一步提高了定位精度和鲁棒性。 实验表明，SBP-YOLO实现了87.0%的mAP，比YOLOv11n基线高出5.8%。 经过TensorRT FP16量化后，在Jetson AGX Xavier上运行速度达到139.5 FPS，比P2增强的YOLOv11快12.4%。 这些结果验证了所提出方法在嵌入式悬挂控制系统中快速且低延迟的道路状况感知的有效性。 显示英文摘要

摘要： Reliable and real-time detection of road speed bumps and potholes is crucial for anticipatory perception in advanced suspension systems, enabling timely and adaptive damping control. Achieving high accuracy and efficiency on embedded platforms remains challenging due to limited computational resources and the small scale of distant targets. This paper presents SBP-YOLO, a lightweight and high-speed detection framework tailored for bump and pothole recognition. Based on YOLOv11n, the model integrates GhostConv and VoVGSCSPC modules into the backbone and neck to reduce computation while enhancing multi-scale semantic features. To improve small-object detection, a P2-level branch is introduced with a lightweight and efficient detection head LEDH mitigating the added computational overhead without compromising accuracy. A hybrid training strategy combining NWD loss, backbone-level knowledge distillation, and Albumentations-driven augmentation further enhances localization precision and robustness. Experiments show that SBP-YOLO achieves 87.0 percent mAP, outperforming the YOLOv11n baseline by 5.8 percent. After TensorRT FP16 quantization, it runs at 139.5 FPS on Jetson AGX Xavier, delivering a 12.4 percent speedup over the P2-enhanced YOLOv11. These results validate the effectiveness of the proposed method for fast and low-latency road condition perception in embedded suspension control systems.