Object detection (OD) is crucial to autonomous driving. Unknown objects are one of the reasons that hinder autonomous vehicles from driving beyond the operational domain. We propose a saliency-based OD algorithm (SalienDet) to detect objects that do not appear in the training sample set. SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation. Then, we design a dataset relabeling approach to differentiate the unknown objects from all objects to achieve open-world detection. We evaluate SalienDet on KITTI, NuScenes, and BDD datasets, and the result indicates that it outperforms existing algorithms for unknown object detection. Additionally, SalienDet can be easily adapted for incremental learning in open-world detection tasks.

翻译：目标检测（OD）对于自动驾驶至关重要。未知对象是阻碍自动驾驶车辆超越运行设计域行驶的原因之一。我们提出一种基于显著性的目标检测算法（SalienDet），用于检测训练样本集中未出现的对象。SalienDet利用基于显著性的算法增强图像特征以生成候选目标区域。随后，我们设计了一种数据集重标注方法，将未知对象与所有对象区分开来，从而实现开放世界检测。我们在KITTI、NuScenes和BDD数据集上评估了SalienDet，结果表明其在未知对象检测方面优于现有算法。此外，SalienDet可轻松适配于开放世界检测任务中的增量学习。