YOLO-ORE: A Deep Learning-Aided Object Recognition Approach for Radar Systems

Tai Yuan Huang, Ming Chun Lee*, Chia Hsing Yang, Ta Sung Lee

*此作品的通信作者

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system. Our proposed system is able to benefit from conventional YOLO and also mitigate the overlap errors and misclassification errors induced by using YOLO. We conduct extensive real-world experiments in realistic scenarios to evaluate our proposed object recognition system. Results validate that our system can provide good performance in complicated real-world scenarios. The results also show that our proposed object recognition system can outperform the state-of-the-art learning-based object recognition systems.

原文English
頁(從 - 到)5715-5731
頁數17
期刊IEEE Transactions on Vehicular Technology
72
發行號5
DOIs
出版狀態Published - 1 5月 2023

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