False negatives (FN) in 3D object detection, which occur when small, distant, or hidden objects are missed, pose significant safety risks in autonomous driving systems. Recent multi-modal fusion methods have been proposed to enhance 3D object detection by combining the geometric accuracy of LiDAR point clouds with the rich semantic features of camera images. However, few methods explicitly address false negatives, and many fail to effectively align and interact multimodal features during the fusion process. To address these challenges, we propose BEVFusion with Dual Hard Instance Probing (BEVFusion-DHIP), a novel 3D object detection framework designed to systematically reduce false negatives. BEVFusion-DHIP incorporates Hard Instance Probing (HIP) into both LiDAR BEV features and 3D position-aware image features, progressively refining the detection of challenging objects across multiple stages. Furthermore, we introduce a Deformable Attention Fusion Network (DAFusionNet) to dynamically align and fuse LiDAR and camera BEV features during the fusion process, effectively mitigating spatial misalignment and enhancing inter-modal feature interaction. Experimental results on the nuScenes dataset show that the proposed BEVFusion-DHIP outperforms state-of-the-art lidar and camera+lidar based 3D object detection models. For example, BEVFusion-DHIP achieves improvements of 3.0 and 3.2 in mAP and NDS, respectively, compared to the baseline model BEVFusion.