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.

本研究提出了一种基于显著性的目标检测算法（SalienDet），用于检测未出现在训练样本集中的目标。该算法利用基于显著性的算法增强图像特征，生成目标提案，并设计了一个数据集标记方法以区分未知目标和所有目标，实现开放世界检测。在KITTI，NuScenes和BDD数据集上评估SalienDet的结果表明，它优于现有的未知目标检测算法。此外，SalienDet可以轻松地适应开放世界检测任务的增量学习。

SalienDet：自动驾驶目标检测的基于显著性特征增强算法