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.
SalienDet: A Saliency-Based Feature Enhancement Algorithm for Object Detection for Autonomous Driving
IEEE Transactions on Intelligent Vehicles ; 9 , 1 ; 2624-2635
01.01.2024
6827916 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Three-Feature Based Automatic Lane Detection Algorithm (TFALDA) for Autonomous Driving
Online Contents | 2003
|Three-Feature Based Automatic Lane Detection Algorithm (TFALDA) for Autonomous Driving
British Library Conference Proceedings | 1999
|Enhancing Autonomous Driving By Exploiting Thermal Object Detection Through Feature Fusion
Springer Verlag | 2024
|