SPD-YOLO: A Novel Lightweight YOLO Model for Road Information Detection

Abstract

Rapid and high-precision speed bump detection is critical for autonomous driving and road safety, yet it faces challenges from non-standard appearances and complex environments. To address this issue, this study proposes a you only look once (YOLO) algorithm for speed bump detection (SPD-YOLO), a lightweight model based on YOLO11s that integrates three core innovative modules to balance detection precision and computational efficiency: it replaces YOLO11s’ original backbone with StarNet, which uses ‘star operations’ to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency; its neck incorporates context feature calibration (CFC) and spatial feature calibration (SFC) to improve detection performance without significant computational overhead; and its detection head adopts a lightweight shared convolutional detection (LSCD) structure combined with GroupNorm, minimizing computational complexity while preserving multi-scale feature fusion efficacy. Experiments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision (mAP) of 79.9%, surpassing YOLO11s by 1.3% and YOLO12s by 1.2% while reducing parameters by 26.3% and floating-point operations per second (FLOPs) by 29.5%, enabling real-time deployment on resource-constrained platforms.

Keywords

lightweight / object detection / road speed bump detection / YOLO11 algorithm