Intelligent Driving Vehicle Object Detection Based on Improved AVOD Algorithm for the Fusion of LiDAR and Visual Information

Abstract

The detection of obstacles of intelligent driving vehicles becomes a primary condition to ensure safe driving. LiDAR and camera are the main sensors for intelligent vehicles to obtain information about their surroundings, and they each have their own benefits in terms of object detection. LiDAR can obtain the position and geometric structure of the object, and camera is very suitable for object recognition, but the reliance on environmental perception by a single-type sensor can no longer meet the detection requirements in complex traffic scenes. Therefore, this paper proposes an improved AVOD fusion algorithm for LiDAR and machine vision sensors. The traditional NMS (non-maximum suppression) algorithm is optimized using a Gaussian weighting method, while the 3D-IoU pose estimation loss function is introduced into the target frame screening module to upgrade the 2D loss function to 3D and design the 3D-IoU criterion. By comparing the detection accuracy of the algorithm proposed in this paper with that of the traditional method, it has been found that the improved AVOD fusion algorithm significantly improved the detection efficiency and the detection accuracy reached 96.3%. The algorithm proposed in this paper can provide a new approach for object detection of intelligent driving vehicles.

Keywords

• intelligent vehicles
• information perception
• object detection
• algorithms