Object detection is the basis of autonomous driving and robot navigation. To solve the problems of insufficient information in 2D images and the large data volume， uneven density， and low detection accuracy of 3D point clouds， a new 3D object-detection network is proposed through an image and point-cloud fusion with deep learning. To reduce the calculation load， the original point cloud is first filtered with the flat interceptor corresponding to the object's frame detected in the 2D image. To address the uneven density， an improved voting model network， based on a generalized Hough transform， is proposed for multiscale feature extraction. Finally， Normal Three-Dimensional Distance Intersection over Union （N3D_DIOU）， a novel loss function， is extended from the Two-Dimensional Distance Intersection over Union （2D DIOU） loss function， which improves the consistency between the generated and target frames， and also improves the object-detection accuracy of the point cloud. Experiments on the KITTI dataset show that our algorithm improves the accuracy of three-dimensional detection by 0.71%， and the aerial-view detection accuracy by 7.28%， over outstanding classical methods.