多模态融合的三维目标检测方法研究OA北大核心CSTPCD

Aiming at the problem that the detection algorithm based on pure point cloud is prone to miss detection and false detection of far-small targets due to the sparsity and disorder of point cloud,a multi-modal 3D object detection algo-rithm combining image features and point cloud voxel features is proposed.In the stage of image feature extraction,a lightweight deep residual network is proposed to reduce the number of image feature channels and make it consistent with the point cloud voxel features,so as to improve the fusion ability of point cloud and image features.In the fusion stage of voxel features and image features,a double feature fusion network is proposed.On the basis of retaining the original voxel feature structure information,the image features and voxel features are fused to make the point cloud have rich semantic information,so as to improve the detection accuracy of far-small targets.The experimental results on the KITTI dataset show that compared with the baseline model,the 3D average detection accuracy of car,cyclist and pedestrian is improved by 0.76 percentage points,2.30 percentage points and 3.43 percentage points,respectively.The experimental results verify the effectiveness of the proposed method for solving the problem of false detection and missed detection of far-small targets.