IFNET: Integrating Data Augmentation and Decoupled Attention Fusion for 3D Object Detection

Abstract

LiDAR is a key sensor for accurately sensing of the environment in autonomous driving. While existing 3D object detection methods generally rely on data augmentation and feature fusion to improve performance, the challenge of dealing with sample imbalance is often overlooked. We design a novel 3D detection network, IFNet, that tackles these issues by introducing mutually reinforcing data augmentation and feature enhancement strategies. It aims to achieve a dual purpose: 1) correcting the category imbalance by directly enhancing pedestrian samples using mixed data augmentation, i.e., RG-Aug; and 2) enhancing feature perception by introducing the decoupling and attention fusion module (DAF). DAF enables robust feature representations across different layers, improving the detection performance, especially for small objects in the scene. Comprehensive experiments on the KITTI dataset and comparisons with state-of-the-art methods demonstrate the superiority of our proposed approach.