PSNS-SSD: Pixel-Level Suppressed Nonsalient Semantic and Multicoupled Channel Enhancement Attention for 3D Object Detection

Abstract

In the field of 3D object detection, voxel-based methods are the most commonly used and exhibit high accuracy. However, point-based networks, which have the capability to preserve the original point features, are unable to surpass the accuracy achieved by voxel-based methods. We observed that during downsampling, too many useless background points are retained in the prevailing farthest point sample, which interferes with the accuracy of the point-based network and introduces redundant computation. To address this issue, in this letter, a point-based single-stage 3D detection network called PSNS-SSD is designed, and its accuracy rivals the accuracy of the two-stage voxel-based methods. Our method leverages the semantic information that suppresses nonsalient features used for guidance and discards background points during the downsampling process. In addition, we propose two modules, namely, linear-conv channel-enhanced self-attention for point cloud feature extraction and global-local coupling attention for center prediction, to enhance the performance and efficiency of our model. Our PSNS-SSD model is extensively tested on the most widely available 3D dataset—the KITTI dataset. The experimental results validate the three proposed modules in terms of accuracy for small object detection and speed, and the model achieves accuracy levels that are on par with the accuracy levels of cutting-edge voxel-based techniques. Moreover, our detector can achieve a speed of 70 FPS on the KITTI dataset, making it a promising candidate for practical applications.