The Impact of Different Backbone Architecture on Autonomous Vehicle
  Dataset
Ning Ding Azim Eskandarian
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.