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In autonomous driving, lidar has become the main vehicle sensor due to its advantages such as long-range measurement and high accuracy. However, the collected point cloud data is sparse and unevenly distributed, and it lacks characterization capabilities when facing objects with missing or similar shapes, so that the detection accuracy is low while detecting long-distance small targets with similar shapes and a small number of point clouds. In order to improve the detection accuracy of small targets represented by point clouds, this paper adopts a method that fuses point cloud and RGB image to construct a 3D object detection network architecture based on two-stage complementary fusion. In the first stage of the fusion, we use the FPS method to select some points from the raw point cloud data as key points. Then, we voxelize the raw point cloud and use the 3D sparse convolutional neural network to extract multi-scale points cloud features, which would fuse features of different scales with key points. In the second stage of fusion, a 2D object detector is used to obtain the 2D bounding box and category information of the target in the image and take the camera as the origin to extend along the direction of the 2D bounding box to form a frustum; then the point cloud and target category information within the frustum are fused into the key points. This paper uses key points as a bridge to effectively combine the semantic information of the image such as texture and color with the point cloud features. The experimental results show that the network proposed in this paper has excellent performance on small objects. [ABSTRACT FROM AUTHOR]