# A LiDAR Multi-Object Detection Algorithm for Autonomous Driving
## 1 Introduction
proposed an end-to-end deep neural network, VoxelNet [8], for 3D object detection in unmanned scenarios, which preserves the original features of the point cloud, but the traditional 3D convolutional network of VoxelNet is computationally cumbersome and slow in operation. In order to improve the 3D convolutional operation speed of the point cloud, [PERSON] et al. proposed the SECOND [15] method, which has good adaptability to complex scenes, and is able to deal with scenes such as vehicles, pedestrians, and buildings almost in real time and efficiently, and at the same time, it is applicable to all kinds of LIDAR data, and it has a better versatility[PERSON] However, the algorithm is based on a sliding window, which will waste some computational resources and reduce the processing efficiency and speed[PERSON] In order to speed up the operation of the algorithm, [PERSON] et al. proposed the PointPillars [16] method, which adopts the point cloud columnar structured features and then transforms them into a two-dimensional pseudo-image, and feature extraction by two-dimensional convolutional neural network, which solves the problem of three-dimensional object detection that cannot be practically applied due to the slow computing speed, and its discarding of the traditional three-dimensional convolutional [17] layer to achieve a detection speed of 62 FPS, which have become the more practical algorithms at present. Although all the above algorithms have achieved some success in the field of point cloud target detection, there are still some limitations and challenges. The sparsity and non-regularity of point cloud data is one of the common challenges of these algorithms, which requires better feature extraction and representation methods, introduction of more powerful models and deep learning frameworks, and optimization of computational algorithms. In summary, the 3D object detection algorithm can detect and analyze the size, shape, and category of targets around the vehicles, provide accurate spatial perception to improve the accuracy and reliability of detection, and plays an important role in the recognition and localization of objects in the automatic driving scene. Therefore, improving the 3D object detection algorithm is of great significance to further enhance the performance and safety of autonomous driving technology. In this paper, for the problem of information loss and inaccurate localization due to the column division of the point cloud in the PointPillars algorithm, the Efficient Channel Attention module [18] is introduced to improve the original algorithm's 2D backbone network. And the contributions of our work are:  
1. Based on the PointPillars algorithm, ECA Modules-PointPillars are proposed by reconstructing the backbone network by connecting the ECA module and the Conv of the down-sampling module in series. 2. The improved algorithm is evaluated on the KITTI datasets, and the visual detection results of the algorithm are also shown for three scenarios: highways, rural roads, and urban roads. 3. The optimization effect of the proposed improved algorithm is analyzed and a preliminary solution is proposed for its insignificant accuracy in terms of pedestrian category performance.