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Cascaded object detection networks for FMCW radars

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Abstract

Object detection using FMCW (Frequency-modulated continuous wave) radars is of massive importance for the advanced driver assistance systems. However, it is exceptionally challenging due to the diversity of the electromagnetic environment and the existence of the class imbalance in the radar data space. In this paper, we propose a cascaded object detection network to achieve accurate object detection using FMCW radars. Consisting of a ROI generation stage and a final detection stage, the proposed cascaded network can tackle the problem of the class imbalance and detect objects from the range-Doppler or range-velocity space effectively. Besides, we propose a range-velocity regression procedure to improve the performance of the range-velocity localization. Extensive simulation experiments demonstrate that our proposed approach can robustly detect objects from noisy electromagnetic environments with a high localization accuracy.

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References

  1. Yadav, R., Dahiya, P.K., Mishra, R.: Comparative analysis of automotive radar sensor for collision detection and warning system. Int. J. Inf. Technol. 12, 289–294 (2020)

    Google Scholar 

  2. Major, B., Fontijne, D., Ansari, A., Sukhavasi, R.T., Gowaikar, R., Hamilton, M., Lee, S., Grechnik, S., Subramanian, S.: Vehicle detection with automotive radar using deep learning on range-azimuth-doppler tensors. In: IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 924–932 (2019)

  3. Magaz, B., Belouchrani, A., Hamadouche, M.: Automatic threshold selection in OS-CFAR radar detection using information theoretic criteria. Progress Electromagn. Res. B 30, 157–175 (2011)

    Article  Google Scholar 

  4. Weinberg, G.V.: An introduction to radar sliding window detectors (2017). arXiv preprint arXiv:1709.09786

  5. Finn, H.M., Johnson, R.S.: Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates. RCA Rev. 29(3), 414–464 (1968)

    Google Scholar 

  6. Xue, W., Sun, X.: Multiple targets detection method based on binary hough transform and adaptive time-frequency filtering. Progress Electromagn. Res. 74, 309–317 (2007)

    Article  Google Scholar 

  7. Chen, S., Li, X.: A new CFAR algorithm based on variable window for ship target detection in SAR images. Signal Image Video Process. 13, 779–786 (2019)

    Article  Google Scholar 

  8. Rohling, H.: Radar CFAR thresholding in clutter and multiple target situations. IEEE Trans. Aerospace Electr. Syst. 4, 608–621 (1983)

    Article  Google Scholar 

  9. Trunk, G.: Range resolution of targets using automatic detectors. IEEE Trans. Aerospace Electr. Syst. 5, 750–755 (1978)

    Article  Google Scholar 

  10. Hansen, V.G., Sawyers, J.H.: Detectability loss due to “greatest of” selection in a cell-averaging CFAR. IEEE Transactions on Aerospace and Electronic Systems 1, 115–118 (1980)

  11. Baadeche, M., Soltani, F., Gini, F.: Performance comparison of mean-level CFAR detectors in homogeneous and non-homogeneous weibull clutter for MIMO radars. Signal Image Video Process. 31, 1677–1684 (2019)

    Article  Google Scholar 

  12. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. TPAMI 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  13. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

  14. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: European Conference on Computer Vision (ECCV), pp. 1–17 (2016)

  15. Long, Z., Suyuan, W., Zhongma, C., Wei, D.: YOLO-RD: A lightweight object detection network for range doppler radar images. In: IOP Conference Series: Materials Science and Engineering, pp. 1–6 (2019)

  16. Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement (2018). arXiv preprint arXiv:1804.02767

  17. Zhang, G., Li, H., Wenger, F.: Object detection and 3d estimation via an FMCW radar using a fully convolutional network. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4487–4491 (2020)

  18. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)

  19. Kärnfelt, C., Pèden, A., Bazzi, A., Shhadè, G.E.H., Abbas, M., Chonavel, T., Bodereau, F.: 77 Ghz ACC radar simulation platform. In: IEEE International Conferences on Intelligent Transport Systems Telecommunications (ITST), pp. 209–214 (2009)

  20. Zhang, W., Li, H., Sun, G., He, Z.: Enhanced detection of doppler-spread targets for FMCW radar. IEEE Trans. Aerospace Electr. Syst. 55(4), 2066–2078 (2019)

    Article  Google Scholar 

  21. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

  22. Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)

  23. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: The thirteenth international conference on artificial intelligence and statistics, pp. 249–256 (2010)

  24. Vedaldi, A., Lenc, K.: MatConvNet—convolutional neural networks for MATLAB. In: ACM Multimedia Conference (ACM MM) (2015)

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Authors and Affiliations

  1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang City, China

    Keyu Lu, Zhisheng Qian, Jiandong Zhu & Manxi Wang

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  1. Keyu Lu
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  2. Zhisheng Qian
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  3. Jiandong Zhu
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  4. Manxi Wang
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Correspondence to Keyu Lu.

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Lu, K., Qian, Z., Zhu, J. et al. Cascaded object detection networks for FMCW radars. SIViP 15, 1731–1738 (2021). https://doi.org/10.1007/s11760-021-01913-6

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  • DOI: https://doi.org/10.1007/s11760-021-01913-6

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