mlsanityData to 14 Oct 2026

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

arXiv 2305.06940 ↗First posted 11 May 2023cs.CVcited by 8

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Abstract (arXiv)

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 urlhttps://github.com/dingmike001/SalienDet-Open-Detection.git.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 41 of its 69 references that are in the map. First posted on arXiv 11 May 2023.

Builds on

41 of 69 references.

References · 41

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29 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 2.

8 papers.