Vision-based autonomous driving requires reliable and efficient object
detection. This work proposes a DiffusionDet-based framework that exploits data
fusion from the monocular camera and depth sensor to provide the RGB and depth
(RGB-D) data. Within this framework, ground truth bounding boxes are randomly
reshaped as part of the training phase, allowing the model to learn the reverse
diffusion process of noise addition. The system methodically enhances a
randomly generated set of boxes at the inference stage, guiding them toward
accurate final detections. By integrating the textural and color features from
RGB images with the spatial depth information from the LiDAR sensors, the
proposed framework employs a feature fusion that substantially enhances object
detection of automotive targets. The $2.3$ AP gain in detecting automotive
targets is achieved through comprehensive experiments using the KITTI dataset.
Specifically, the improved performance of the proposed approach in detecting
small objects is demonstrated.

一项基于视觉的自动驾驶需要可靠高效的对象检测的研究提出了一种 DiffusionDet 框架，该框架利用单目相机和深度传感器的数据融合来提供 RGB 和深度（RGB-D）数据，并通过在训练阶段随机重塑基准边界框，使模型学习噪声加入的相反扩散过程。通过将 RGB 图像的纹理和颜色特征与 LiDAR 传感器的空间深度信息结合起来，所提出的框架采用了特征融合，从而大大提高了汽车目标的对象检测能力。在 KITTI 数据集上进行的全面实验取得了 2.3 的 AP 增益，特别是在检测小物体方面展示了所提出方法的改进性能。