An improved algorithm based on the YOLOv8n model is proposed to address the challenge of detection accuracy for electric vehicles entering elevators in complex scenarios.First, the backbone network integrates the C2f module with the universal inverted bottleneck structure to form a new C2f_UIB module, optimizing computational efficiency and reducing parameters while improving global information capture capability. Additionally, a spatial-channel synergistic attention (SCSA) module is added to the backbone to enhance feature extraction and model robustness. Second, the neck network is reconstructed using an improved re-parameterized generalized feature pyramid network (DSRepGFPN), which enhances cross-scale feature fusion, improves multi-scale object detection performance, and reduces computational complexity. Finally, the original CIOU loss function is replaced with MPDIOU, improving bounding box localization accuracy, especially in scenarios with lighting variations and object occlusions. Experimental results on an elevator electric vehicle dataset show that the proposed YOLOv8-UAR algorithm achieves a 2.5% improvement in mAP50 and a 1.8% improvement in mAP50.95 compared to YOLOv8n, with a detection speed of 94 fps. This makes it suiTable for deployment on edge devices, aligning better with practical requirements for electric vehicle elevator detection.