<p>This study investigates and enhances the accuracy of real object detection in service robots, particularly in environments where printed images or advertisements may interfere with the detection of real-world objects. A hybrid approach is proposed, combining YOLO models for object detection, depth estimation techniques for spatial understanding, and edge-texture analysis for distinguishing real objects from images. The COCO dataset, containing 2D images, and ShapeNet, providing 3D models, were used to construct a custom evaluation dataset for benchmarking the proposed hybrid approach using pretrained YOLOv8 and MiDaS models. Experimental results show that the integration of depth estimation and edge‑texture analysis achieved an accuracy of 86% in identifying real objects, representing a 6 percentage-point improvement over the YOLOv8 baseline. The proposed method effectively enhances the ability of service robots to perform tasks accurately in complex and dynamic environments.</p>