SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving
This addresses the challenge of unknown objects hindering autonomous vehicles, but it appears incremental as it builds on existing object detection frameworks.
The paper tackles the problem of detecting unknown objects in autonomous driving by proposing SalienDet, a saliency-based feature enhancement algorithm, which outperforms existing methods on KITTI, nuScenes, and BDD datasets.
Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder autonomous vehicles from driving beyond the operational domain. To addresss this issue, we propose a saliency-based OD algorithm (SalienDet) to detect unknown objects. Our SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation. Moreover, we design a dataset relabeling approach to differentiate the unknown objects from all objects in training sample set to achieve Open-World Detection. To validate the performance of SalienDet, we evaluate SalienDet on KITTI, nuScenes, and BDD datasets, and the result indicates that it outperforms existing algorithms for unknown object detection. Notably, SalienDet can be easily adapted for incremental learning in open-world detection tasks. The project page is \url{https://github.com/dingmike001/SalienDet-Open-Detection.git}.