轻量级3D目标检测模型，效率提升显著且精度不降。
A Light Perspective for 3D Object Detection
- 用轻量级网络替代传统骨干，提升计算效率
- 在KITTI上比MobileNetV3少90%参数，精度高2.39%
- 适合车载系统部署，尤其对资源受限场景友好
理解环境并准确检测三维空间中的物体是推动自动驾驶技术发展的关键。融合相机与激光雷达数据已成为实现高精度3D目标检测的有效方法。然而，现有方法常依赖计算开销大的传统骨干网络。本文提出一种新方法，将前沿深度学习技术融入特征提取过程，旨在构建高效模型而不牺牲性能。所提模型NextBEV在KITTI 3D单目检测基准上优于ResNet50和MobileNetV2，参数量不足MobileNetV3的10%，精度提升2.39%。此外，改进的激光雷达骨干网络将推理时间降至10毫秒。通过融合这些轻量级特征，使基于VoxelNet的模型精度提升2.93%，点云柱状模型（PointPillar）的F1分数提高约20%。本工作为独立或融合技术提供了轻量且强大的模型，更适用于车载部署。
原文摘要 · Abstract (English)
Comprehending the environment and accurately detecting objects in 3D space are essential for advancing autonomous vehicle technologies. Integrating Camera and LIDAR data has emerged as an effective approach for achieving high accuracy in 3D Object Detection models. However, existing methodologies often rely on heavy, traditional backbones that are computationally demanding. This paper introduces a novel approach that incorporates cutting-edge Deep Learning techniques into the feature extraction process, aiming to create more efficient models without compromising performance. Our model, NextBEV, surpasses established feature extractors like ResNet50 and MobileNetV2. On the KITTI 3D Monocular detection benchmark, NextBEV achieves an accuracy improvement of 2.39%, having less than 10% of the MobileNetV3 parameters. Moreover, we propose changes in LIDAR backbones that decreased the original inference time to 10 ms. Additionally, by fusing these lightweight proposals, we have enhanced the accuracy of the VoxelNet-based model by 2.93% and improved the F1-score of the PointPillar-based model by approximately 20%. Therefore, this work contributes to establishing lightweight and powerful models for individual or fusion techniques, making them more suitable for onboard implementations.
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