Road Abandoned Objects (RAOs) pose significant threats to traffic safety, particularly due to their small size, irregular shapes, and unpredictable distribution in complex road environments. The primary objective of this study is to develop an accurate and real-time detection framework for RAOs while maintaining low computational cost for practical deployment. To achieve this, we propose RAO-YOLO, a lightweight vision-based detection framework built upon an enhanced YOLO architecture. Specifically, a Mixed Aggregation Network (MANet) is introduced to improve multi-scale feature representation, and a Lightweight Shared Detail-Enhanced Detection (LSDD) head is designed to enhance localization accuracy for small and irregular objects. Furthermore, a Focal-MPDIoU loss function is proposed to address sample imbalance and geometric irregularity during training. Extensive experiments conducted on the RAOD dataset demonstrate that the proposed method achieves superior performance compared to state-of-the-art detectors, achieving a mAP@0.5:0.95 of 56.1% while maintaining real-time inference speed. These results validate the effectiveness of the proposed framework for practical intelligent transportation applications.