3D Object Detection in Autonomous Driving Scenarios Based on Tv-SECOND
In response to the issue of 3D object detection tasks in complex environments of autonomous driving scenarios,particu-larly under long-distance and occlusion conditions,a two-stage Tv-SECOND algorithm is proposed based on SECOND to enhance detec-tion accuracy.This algorithm introduces a proposal feature extraction module with a Transformer architecture,and additionally,proposes a deformable voxel feature encoding module based on traditional voxel feature encoding.Tested on the KITTI dataset,results show that compared to SECOND,our proposed algorithm improves the detection performance by 7.49％and 9.72％respectively in long-distance and severe occlusion situations.Moreover,it exhibits varying degrees of improvement in detection accuracy compared to other advanced two-stage methods,demonstrating the effectiveness of the Tv-SECOND algorithm.This new algorithm can establish dependencies among features,aggregate wide-ranging contextual information from surrounding areas,and enhance learning and inference capabilities.It effec-tively improves the detection performance of the model in long-distance and occluded scenarios.
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