Autonomous driving represents the future of transportation, and the precise detection of three-dimensional (3D) objects is a fundamental requirement for achieving autonomous driving capabilities. Presently, 3D object detection primarily relies on sensors, such as monocular cameras, stereo cameras, and LiDAR technology. In comparison to stereo cameras and LiDAR, monocular 3D object detection offers the advantages of a wider field of view and reduced cost. However, the existing monocular 3D object detection techniques exhibit limitations in terms of accuracy, particularly when detecting distant objects. To tackle this challenge, we introduce an innovative approach for monocular 3D object detection, specifically tailored for distant objects. The proposed method classifies objects into distant and nearby categories based on the initial depth estimation, employing distinct feature enhancement and refinement modules for each category. Subsequently, it extracts 3D features and, ultimately, derives precise 3D detection bounding boxes. Experimental results using the KITTI dataset demonstrate that this approach substantially enhances the detection accuracy of distant objects while preserving the detection efficacy for nearby objects.