LVD-YOLO: An efficient lightweight vehicle detection model for intelligent transportation systems

Abstract

Vehicle detection is a fundamental component of intelligent transportation systems. However, current algorithms often encounter issues such as high computational complexity, long execution times, and significant resource demands, making them unsuitable for resource-limited environments. To overcome these challenges, we propose LVD-YOLO, a Lightweight Vehicle Detection Model based on YOLO. This model incorporates the EfficientNetv2 network structure as its backbone, which reduces parameters and enhances feature extraction capabilities. By utilizing a bidirectional feature pyramid structure along with a dual attention mechanism, we enable efficient information exchange across feature layers, thereby improving multiscale feature fusion. Additionally, we refine the model's loss function with SIoU loss to boost regression and prediction performance. Experimental results on the PASCAL VOC and MS COCO datasets show that LVD-YOLO outperforms YOLOv5s, achieving a 0.5% increase in accuracy while reducing FLOPs by 64.6% and parameters by 48.6%. These improvements highlight its effectiveness for use in resource-constrained environments.