A Method for Lightweight Pedestrian and Vehicle Detection for Unmanned Ground Vehicles in Open Environments

In open environments, lightweight pedestrian and vehicle detection models deployed on edge platforms of Unmanned Ground Vehicles (UGVs) often struggle to balance detection accuracy and inference efficiency when facing complex backgrounds, distant small targets, and occluded objects. To address this, we propose a lightweight object detection model based on YOLO11n, named UGV-Net. This model enhances feature interaction and global context modeling capabilities by introducing the C3k2_PS module, employs Dysample dynamic upsampling to achieve content-aware feature reconstruction, and designs an LSDECD detection head to reduce multi-scale prediction redundancy and computational overhead, thereby balancing detection accuracy and inference efficiency. Compared with the baseline model YOLO11n, UGV-Net improves F1 score, mAP50, and mAP50:95 by 2.19%, 2.26%, and 2.10%, respectively, on the KITTI dataset, while reducing GFLOPs from 6.3 to 4.9 and the number of parameters from 2.58M to 2.41M. Similarly, on the SODA10M and FLIR datasets, the F1 score improves by 1.71% and 1.41%, and mAP50 improves by 1.67% and 2.39%, respectively, demonstrating excellent detection accuracy and generalization ability. Furthermore, experiments on the Jetson Orin Nano platform verify that UGV-Net achieves robust real-time detection performance, making it an efficient, reliable, and lightweight solution for UGV perception in open environments.