To promote the development of autonomous driving technology, this study addresses the poor detection performance and low accuracy of existing vehicle detection algorithms for small-sized targets by proposing QF-YOLOv5, an improved YOLOv5-based vehicle detection algorithm. Building upon the YOLOv5 architecture, the following enhancements are introduced: An additional small-scale feature fusion detection layer is incorporated to enhance the detection capability for small targets. An attention mechanism is integrated to guide the network to focus on effective features while suppressing irrelevant ones, thereby improving detection performance. Depthwise separable convolution is adopted to reduce computational complexity. The Mini Batch K-Means clustering algorithm is employed to accelerate network convergence. The Quality Focal loss function is utilized to enable supervised learning for continuous numerical predictions. Experimental results demonstrate that the proposed algorithm achieves improvements in both detection accuracy and real-time performance.