@article{hsu_lai_zhong_yin_perng_2019, 
  title={Developing an On-Road Object Detection System Using Monovision and Radar Fusion}, 
  volume={13}, 
  DOI={10.3390/en13010116}, 
  abstractNote={<jats:p>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.</jats:p>}, 
  publisher={MDPI AG}, 
  author={Hsu, Ya-Wen and Lai, Yi-Horng and Zhong, Kai-Quan and Yin, Tang-Kai and Perng, Jau-Woei}, 
  year={2019}, 
  month={Dec}
  }