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

• URL: http://arxiv.org/abs/2508.01339v1
• Date: Sat, 02 Aug 2025 12:15:08 GMT
• Title: SBP-YOLO:A Lightweight Real-Time Model for Detecting Speed Bumps and Potholes
• Abstract summary: This paper proposes SBP-YOLO, a lightweight detection framework based on YOLOv11, optimized for embedded deployment.<n>The model integrates GhostConv for efficient computation, VoVGSCSPC for multi-scale feature enhancement, and a Efficiency Detection (LEDH) to reduce early-stage feature processing costs.
• Score: 30.847931539927753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With increasing demand for ride comfort in new energy vehicles, accurate real-time detection of speed bumps and potholes is critical for predictive suspension control. This paper proposes SBP-YOLO, a lightweight detection framework based on YOLOv11, optimized for embedded deployment. The model integrates GhostConv for efficient computation, VoVGSCSPC for multi-scale feature enhancement, and a Lightweight Efficiency Detection Head (LEDH) to reduce early-stage feature processing costs. A hybrid training strategy combining NWD loss, knowledge distillation, and Albumentations-based weather augmentation improves detection robustness, especially for small and distant targets. Experiments show SBP-YOLO achieves 87.0% mAP (outperforming YOLOv11n by 5.8%) and runs at 139.5 FPS on a Jetson AGX Xavier with TensorRT FP16 quantization. The results validate its effectiveness for real-time road condition perception in intelligent suspension systems.

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