联合多视角互投影融合的三维目标检测方法

Aiming at the lack of feature fusion of multi-sensor target regions in the current target detection of intelligent vehicles, a three-dimensional target detection method was proposed based on multi-modal information fusion. Firstly, taking the image view and aerial view of lidar point cloud as input, the target detection was optimized by an improved AVOD deep learning network algorithm. And then, a multi-angle joint loss function was inducted to prevent the branch network image degradation. Finally, a dual-view image and the lidar point cloud projected mutual fusion method was presented to enhance data spatial correlation and to carry out feature fusion. The experimental results show that the improved AVOD-MPF network can improve the detection accuracy of small-scale targets while retaining the advantages of the AVOD network for vehicle target detection, and achieve 3D target detection with feature-level and decision-level fusion.