The detection speed and accuracy of detecting targets ahead during vehicle operation have always been a focus of research in the field of autonomous driving. For existing object detection algorithm models, in complex traffic environments, traditional models are prone to false positives and missed detections when facing overlapping targets. Significantly improving detection accuracy can also lead to increased computational demands, resulting in slower processing speed and decreased real-time performance. This article proposes an improved algorithm based on the YOLOv5 model. Firstly, the MobileNetV3 network is adopted to replace the C3 backbone network in the original model, achieving a lightweight network while improving the model’s response speed. Secondly, a non-maximum suppression algorithm, Adaptive-EIoU-NMS, is proposed to improve the recognition accuracy of overlapping targets. Finally, the K-means++ clustering algorithm is used to replace the original clustering algorithm and generate more accurate anchor boxes. Experimental results show that the improved model achieves an average detection accuracy of 90.1% and a detection speed of 89 frames per second (f/s). The experimental results confirm that the enhanced model significantly improves both detection accuracy and speed for complex scene detection.