The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset
This work provides insights for autonomous driving researchers by benchmarking backbone architectures in real-world driving environments, though it is incremental as it applies existing methods to new data.
The study evaluated the performance of different backbone architectures on object detection tasks using three autonomous vehicle datasets (KITTI, NuScenes, BDD), addressing a gap in prior research focused on generic datasets.
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.