As agricultural machinery evolves towards intelligence and automation, obstacle detection in agricultural environments becomes crucial for safe operations of intelligent agricultural machinery. Pedestrians, as one of the most common obstacles in orchards, usually exhibit autonomy and behavioral unpredictability. Therefore, the development of intelligent agriculture requires reliable pedestrian detection technology. To address this, we propose ReB-DINO, a robust and accurate orchard pedestrian object detection model based on an improved DINO. Initially, we improve the feature extraction module of DINO using structural re-parameterization, enhancing accuracy and speed of the model through training and decoupling inference. In addition, a progressive feature fusion module is employed to fuse the extracted features and improve model accuracy. Finally, the network incorporates a convolutional block attention mechanism and an improved loss function to improve pedestrian detection rates. The experimental results demonstrate a 1.6% improvement in Recall on the NREC dataset compared to the baseline. Moreover, the results show a 4.2% improvement in mAP and the number of parameters decreases by 40.2% compared to the original DINO, enhancing accuracy and real-time object detection in apple orchards while maintaining lightweight attributes, surpassing mainstream object detection models.