Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Ya-Wen Hsu, Yi-Horng Lai, Kai-Quan Zhong, Tang-Kai Yin and Jau-Woei Perng
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Ya-Wen Hsu: Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
Yi-Horng Lai: School of Mechanical and Electrical Engineering, Xiamen University Tan Kah Kee College, Zhangzhou 363105, China
Kai-Quan Zhong: Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
Tang-Kai Yin: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 81148, Taiwan
Jau-Woei Perng: Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan

Energies, 2019, vol. 13, issue 1, 1-18

Abstract: In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

Keywords: particle filter; histogram of gradient; sensor fusion; neural network; support vector machine; object recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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