SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving
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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.
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