YOLO-FSE: An Improved Target Detection Algorithm for Vehicles in Autonomous Driving

Real-time object detection plays a critical role in advancing autonomous driving technologies. To meet the demands for real-time performance, a lightweight model is essential to reduce parameter size, simplify complexity, and enhance detection speed. This article presents YOLO-FSE, a compact vehicle detection model built upon the YOLOv5 framework. In YOLO-FSE, the C3 module is substituted with the C3_faster module from the FasterNet lightweight architecture. The backbone network is augmented with the Shuffle Attention module, which improves feature fusion by separating spatial and channel attention mechanisms. Additionally, the original loss function is replaced by EIoU loss, which directly penalizes width and height predictions, thereby improving the model’s generalization ability. During experimentation, it was assumed that the number of test images was sufficiently large and clear, with PyTorch serving as the primary framework for model development and training. Experimental results show that, on the UA-DETRAC and BIT-Vehicle datasets, the mean average precision (mAP) achieved 98.9% and 97.6%, respectively. Computational complexity (FLOPs) was reduced by 12.65%, while the average frame rate (Fps) increased by 46.13%. Overall, the enhanced model exhibits substantial improvements in both speed and performance, demonstrating its potential for deployment in autonomous driving applications.