Year: 2025
Type: conference-paper
Abstract: With the continuous improvement of hardware device performance and the development of artificial intelligence technology, radar point clouds have become an important component in related fields such as autonomous driving due to their high anti-interference ability. However, existing detection technologies still pose a challenge in identifying small objects. The algorithm first adopts a feature pyramid structure to address the size difference between detected targets in actual 3D object detection scenes. Through multi-scale fusion, the model can retain more delicate features during the encoding stage. In the feature extraction stage, a cascade approach of deformable convolution and self-attention mechanism was adopted. The deformable convolution can adaptively change the shape and size of the input features to filter out noise effects. The self-attention mechanism is used to consider the global information of all pixels in the image, helping the model better understand the image content and improve the accuracy of object detection. The experimental results show that the proposed algorithm performs well in simple, medium, and difficult levels in BEV mode, 3D mode, and AOS mode. Compared with the original algorithm, the average accuracy has been significantly improved, especially in the difficulty level of BEV mode, where the improvement rates for cars, pedestrians, and bicycles reach 5.75%, 3.56%, and 7.05%, respectively.
Authors: Qidi Zhou, Wei Li
Institution: Xiamen University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G4255957075
OpenAlex ID: W4413812547