<p>Robust monocular 3D object detection remains a pivotal challenge for intelligent robotic systems due to the absence of explicit depth information in single RGB images. In this paper, we propose a novel depth-guided attention enhancement (DGAE) module, integrated into the MonoDTR framework, to address its limitations in handling noisy depth supervision and refining spatial inconsistencies. DGAE leverages coarse depth maps as attention priors to guide visual feature refinement through temperature-scaled softmax and Gaussian smoothing, enabling enhanced spatial reasoning and robustness in cluttered scenes. To support this attention mechanism, we generate high-quality depth maps by projecting LiDAR points into the image plane and interpolating missing regions using a nearest-neighbor approach, followed by bilateral filtering and block downsampling to preserve edge details while reducing noise. This depth estimation pipeline improves the quality and coherence of the fused features used by DGAE. Extensive experiments on the KITTI 3D object detection benchmark show that our approach achieves state-of-the-art performance in moderate and hard detection scenarios for cars, pedestrians, and cyclists, while maintaining real-time inference speeds. These results underscore the effectiveness and practicality of our DGAE module for real-world 3D perception in autonomous driving applications.</p>