ESFormer: A Pillar-Based Object Detection Method Based on Point Cloud Expansion Sampling and Optimised Swin Transformer
In recent years, with the wide application of autonomous driving, surveillance, and robotics, the demand for accurate object detection in efficient object scenarios has surged.However, traditional object detection methods often face the challenge of difficulty in balancing detection accuracy and processing speed when dealing with the dynamic charac