An improved YOLOv11 real-time object detection algorithm is proposed to address the detection difficulties and inaccurate positioning of small and occluded targets in complex dynamic scenarios of intelligent vehicles. Firstly, in response to the difficulty of small object recognition caused by the loss of pooling layer features in the backbone network, DSEAIFI was proposed on the basis of AIFI to replace the pooling layer in the backbone network. Secondly, in order to improve the neck network′s ability to utilize and fuse features, as well as enhance its ability to detect occluded targets, the MFFNeck network was proposed, which improved the model′s ability and adaptability to fuse contextual features. Finally, in order to further improve the adaptability of the network to complex dynamic environments and highlight the importance level of advanced features in the feature map, a LAAFPN network designed for the detection head was integrated into the head network. To verify the performance of the proposed algorithm, simulations and real vehicle experiments were conducted, and the simulation results showed that the improved algorithm was effective on the KITTI dataset mAP@0.5 and mAP@0.5 0.95 is 91.1% and 70.1% respectively, which is an improvement of 2.1% and 3.8% compared to the basic model. The actual vehicle experiment results show that the average detection accuracy of the proposed algorithm is 92.7%, which is 4.3% higher than the basic model and has good real-time performance.