Object detection is a crucial component of autonomous driving, and many detection applications have been developed to address this task. These applications often rely on backbone architectures, which extract representation features from inputs to perform the object detection task. The quality of the features extracted by the backbone architecture can have a significant impact on the overall detection performance. Many researchers have focused on developing new and improved backbone architectures to enhance the efficiency and accuracy of object detection applications. While these backbone architectures have shown state-of-the-art performance on generic object detection datasets like MS-COCO and PASCAL-VOC, evaluating their performance under an autonomous driving environment has not been previously explored. To address this, our study evaluates three well-known autonomous vehicle datasets, namely KITTI, NuScenes, and BDD, to compare the performance of different backbone architectures on object detection tasks.

翻译：目标检测是自动驾驶中的关键组成部分，目前已开发出多种检测应用来处理该任务。这些应用通常依赖于主干网络架构，通过从输入中提取表征特征来完成目标检测。主干网络所提取特征的质量对整体检测性能具有显著影响。许多研究者致力于开发新型和改进的主干网络架构，以提升目标检测应用的效率与精度。尽管这些主干网络在MS-COCO和PASCAL-VOC等通用目标检测数据集上展现出最先进的性能，但尚未有研究评估其在自动驾驶环境下的表现。为解决这一问题，本研究评估了KITTI、NuScenes和BDD三个知名自动驾驶数据集，以比较不同主干网络架构在目标检测任务上的性能。