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

• Published in: Energies (Basel) 2020-01, Vol.13 (1), p.116
• Main Authors: Hsu, Ya-Wen, Lai, Yi-Horng, Zhong, Kai-Quan, Yin, Tang-Kai, Perng, Jau-Woei
• Format: Article
• Language: English
• Subjects: Algorithms; Cameras; Clustering; Collision avoidance; False alarms; histogram of gradient; Lasers; Millimeter waves; Motorcycles; neural network; Neural networks; Noise; object recognition; particle filter; Pedestrians; Radar; Radar detection; Radial basis function; Roads & highways; sensor fusion; Sensors; support vector machine; Support vector machines; Systems stability; Vehicles; Warning systems
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13010116

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.