RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

Dalbah, Yahia; Lahoud, Jean; Cholakkal, Hisham

doi:10.1007/978-3-031-31435-3_23

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Additionally, in contrast to radar-based odometry and localization, the broader field of sensor perception is witnessing a focus on advanced deep learning techniques. These include variations in transformers [153,154], the utilization of various point feature encoder [112,155] architectures, and the incorporation of rotational invariance into deep learning modules [156,157] through model architectures instead of augmentations during training. These advancements are influencing not only general radar perception but also odometry and localization models across other modalities.…”

Section: Discussion and Future Researchmentioning

confidence: 99%

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Brune,

Meisen,

Pomp

2024

Applied Sciences

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This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

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Section: Discussion and Future Researchmentioning

confidence: 99%

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Brune,

Meisen,

Pomp

2024

Applied Sciences

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“…Paek et al [27] proposed a 4D mmw dataset named K-Radar and a baseline deep neural network to detect objects on roads. Yahia et al [28] developed a lightweight deep learning model named RadarFormer to identify objects in real time.…”

Section: Autonomous Driving and Transportationmentioning

confidence: 99%

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

et al. 2023

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Digital construction relies on effective sensing to enhance the safety, productivity, and quality of its activities. However, current sensing devices (e.g., camera, LiDAR, infrared sensors) have significant limitations in different aspects. In light of the substantial advantages offered by emerging 4D mmw technology, it is believed that this technology can overcome these limitations and serve as an excellent complement to current construction sensing methods due to its robust imaging capabilities, spatial sensing abilities, velocity measurement accuracy, penetrability features, and weather resistance properties. To support this argument, a scientometric review of 4D mmw-based sensing is conducted in this study. A total of 213 articles published after the initial invention of 4D mmw technology in 2019 were retrieved from the Scopus database, and six kinds of metadata were extracted from them, including the title, abstract, keywords, author(s), publisher, and year. Since some papers lack keywords, the GPT-4 model was used to extract them from the titles and abstracts of these publications. The preprocessed metadata were then integrated using Python and fed into the Citespace 6.2.R3 for further statistical, clustering, and co-occurrence analyses. The result revealed that the primary applications of 4D mmw are autonomous driving, human activity recognition, and robotics. Subsequently, the potential applications of this technology in the construction industry are explored, including construction site monitoring, environment understanding, and worker health monitoring. Finally, the challenges of adopting this emerging technology in the construction industry are also discussed.

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Hardware-aware approach to deep neural network optimization

Li,

Meng

2023

Neurocomputing

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RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

Cited by 7 publications

References 32 publications

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

Hardware-aware approach to deep neural network optimization

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