SBD-YOLO: An Efficient Speed Bump Detector for Active Suspension Preview Control

Speed bump detection through computer vision and deep learning is essential for advancing active suspension preview control and intelligent driving. Although substantial progress has been made in this field, there remains a need to enhance detection accuracy while reducing computational demands. This article introduces a novel single-stage speed bump detector, the Speed Bump Detector Based on You Only Look Once (SBD-YOLO), which utilizes the YOLOv9 architecture for speed bump identification. To better capture the deep global features of speed bumps, we propose an innovative convolutional module—specifically, a lightweight building block designed for efficient feature extraction—named the Aggregated-MBConv. Furthermore, we design a new YOLO backbone by stacking Mobile Inverted Bottleneck Convolution (MBConv) and Aggregated-MBConv modules, which reduces computational cost while enhancing detection accuracy. Additionally, we introduce a Squeeze-aggregated Excitation (SaE) attention mechanism at the network’s neck, which, through parallel operation, enables collective integration across branches, further improving network performance. A dedicated speed bump dataset was created to validate SBD-YOLO’s effectiveness. Compared to YOLOv9, SBD-YOLO achieves a 9.3% increase in precision, a 2.5% boost in recall, and improvements of 2.2% and 1.4% in mean Average Precision at an Intersection-over-Union (IoU) threshold of 50% (mAP50) and mean Average Precision over IoU thresholds from 50% to 95% (mAP50-95), respectively. Moreover, the number of parameters is reduced by 5 million, and computational complexity is decreased by approximately 82.8%. These results demonstrate the significant potential of SBD-YOLO for active suspension preview control.