SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes toward Intelligent Vehicle Suspension Systems

TL;DR

SBP-YOLO is a lightweight, real-time detection framework based on YOLOv11n designed for embedded systems to accurately identify speed bumps and potholes, improving vehicle suspension response with high speed and low computational cost.

Contribution

The paper introduces SBP-YOLO, a novel detection framework that integrates GhostConv, VoVGSCSPC modules, and a hybrid training strategy to enhance small-object detection and efficiency in embedded environments.

Findings

Achieves 87.0% mAP, outperforming baseline by 5.8%.

Runs at 139.5 FPS on Jetson AGX Xavier after quantization.

Demonstrates suitability for real-time road anomaly detection in embedded systems.

Abstract

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.