Vehicle classification applying many‐to‐one input network architecture in 77‐GHz FMCW radar. Issue 2 (15th October 2021)

• Record Type: Journal Article
• Authors: Jiang, Haodong; Zhang, Li; Wang, Ke; Guo, Yaozu; Yan, Feng; Ji, Xiaoli
• Is Part Of: IET radar, sonar & navigation. Volume 16:Issue 2(2022)
• Page Start: 267
• Page End: 277
• Publication Date: 2021-10-15
• Subjects: convolutional neural nets -- image classification -- image processing -- radar signal processing -- road vehicles; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/rsn2.12181
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract: In this paper, we proposed a Many‐to‐One Input Network Architecture (MOINA) for the classification of similar structured vehicles (bus, truck and car). The inputs of the architecture are the multiple‐masked region‐of‐interest of the same detected vehicle from Range‐Doppler maps, which are acquired by FMCW radar. The proposed method is trained with a supervised system yielding a classification accuracy of 98%. MOINA shows good classification performance in a practical situation. Besides, the F1‐score of buses, trucks and cars are 98.7%, 98.0% and 97.6%, respectively.