Laser point cloud three-dimensional target detection model and method for complex traffic scene

Abstract

The invention discloses a laser point cloud three-dimensional target detection model and method for a complex traffic scene, a three-dimensional encoder in the model is beneficial to the detection of long-distance targets and small targets, and sparse convolution and sub-manifold convolution can greatly improve the coding efficiency of voxel features. The residual structure of the two-dimensional encoder can keep more complete information, detection of a long-distance target and a small target is facilitated, meanwhile, the network is easier to optimize, feature extraction and receptive field expansion can be carried out on an original scale and a self-calibration scale through self-calibration convolution, more complete and rich features can be extracted. Useful feature expression is enhanced in the space direction and the channel direction through space attention and channel attention, and useless information is inhibited. The final detection precision of vehicles is 81.88%, the final detection precision of pedestrians is 47.82%, the final detection precision of riders is 69.81%, the average precision is 66.25%, the average precision is 9.9% higher than that of an existing VOXEL RCNN algorithm, 13.8 FPS is achieved on RTX 2080Ti display, and the detection precision and speed can meet the sensing requirements of intelligent vehicles in complex traffic environments.

Description

technical field [0001] The invention belongs to the field of intelligent vehicle perception, and specifically refers to a laser point cloud three-dimensional target detection model and method for complex traffic scenes. Background technique [0002] A smart car is a complex system including perception, decision-making, and control. Among them, environmental perception technology is the basis of intelligent vehicles, which provides necessary environmental information for subsequent decision-making and control. The accuracy of traditional machine learning algorithms can no longer meet the operating requirements of current smart cars. As a result, deep learning-based perception algorithms have developed rapidly and made great progress in the field of 2D object detection and segmentation. However, the camera will be affected by night, rain, fog, strong light and other conditions, which will affect the detection effect. With the reduction of the cost of lidar and the improveme...

AI Technical Summary

Problems solved by technology

[0003] In view of the fact that most of the current 3D target detection algorithms cannot adapt to complex traffic scenes, the detection of long-distance vehicles and short-distance pedestrians, cyclists and other small targets is not effective, and cannot meet the perception needs of smart cars in complex traffic situations.

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