Abstract
The reliable obstacle detection is a challenging task in autonomous navigation of unmanned surface vehicles. In this paper, we present a novel real-time obstacles detection based on monocular vision which can effectively tell apart obstacles on the sea surface from complex background. The main innovation of this paper is to propose a water-boundary-line algorithm based on semantic segmentation and random sample consistency line fitting. And use a simple and effective saliency detection method based on background prior and foreground prior to detect obstacles under the water-boundary-line. Our method can efficiently and quickly obtain obstacle information from images captured by shipborne cameras, and it has the ability to process more than 33 frames/s.
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References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Almeida, C., et al.: Radar based collision detection developments on USV ROAZ II. In: Oceans 2009-Europe, pp. 1–6. IEEE (2009)
Bovcon, B., Perš, J., Kristan, M., et al.: Improving vision-based obstacle detection on USV using inertial sensor. In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, pp. 1–6. IEEE (2017)
Bovcon, B., Perš, J., Kristan, M., et al.: Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation. Robot. Auton. Syst. 104, 1–13 (2018)
Gal, O.: Automatic obstacle detection for USV’s navigation using vision sensors. In: Schlaefer, A., Blaurock, O. (eds.) Robotic Sailing, pp. 127–140 . Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22836-0_9
Gal, O., Zeitouni, E.: Tracking objects using PHD filter for USV autonomous capabilities. In: Sauze, C., Finnis, J. (eds.) Robotic Sailing 2012, pp. 3–12. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-33084-1_1
Guo, Y., Romero, M., Ieng, S.H., Plumet, F., Benosman, R., Gas, B.: Reactive path planning for autonomous sailboat using an omni-directional camera for obstacle detection. In: 2011 IEEE International Conference on Mechatronics, pp. 445–450. IEEE (2011)
Heidarsson, H.K., Sukhatme, G.S.: Obstacle detection from overhead imagery using self-supervised learning for autonomous surface vehicles. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3160–3165. IEEE (2011)
Kristan, M., Kenk, V.S., Kovačič, S., Perš, J.: Fast image-based obstacle detection from unmanned surface vehicles. IEEE Trans. Cybern. 46(3), 641–654 (2015)
Li, C., Cao, Z., Xiao, Y., Fang, Z.: Fast object detection from unmanned surface vehicles via objectness and saliency. In: 2015 Chinese Automation Congress (CAC), pp. 500–505. IEEE (2015)
Mou, X., Wang, H.: Wide-baseline stereo-based obstacle mapping for unmanned surface vehicles. Sensor 18(4), 1085 (2018)
Wang, H., Wei, Z.: Stereovision based obstacle detection system for unmanned surface vehicle. In: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 917–921. IEEE (2013)
Wang, H., Wei, Z., Ow, C.S., Ho, K.T., Feng, B., Huang, J.: Improvement in real-time obstacle detection system for USV. In: 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1317–1322. IEEE (2012)
Wang, H., Wei, Z., Wang, S., Ow, C.S., Ho, K.T., Feng, B.: A vision-based obstacle detection system for unmanned surface vehicle. In: 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM), pp. 364–369. IEEE (2011)
Wang, H., et al.: Real-time obstacle detection for unmanned surface vehicle. In: 2011 Defense Science Research Conference and Expo (DSR), pp. 1–4. IEEE (2011)
Wang, J., Gu, W., Zhu, J., Zhang, J.: An unmanned surface vehicle for multi-mission applications. In: 2009 International Conference on Electronic Computer Technology, pp. 358–361. IEEE (2009)
Yan, R.J., Pang, S., Sun, H.B., Pang, Y.j.: Development and missions of unmanned surface vehicle. J. Marine Sci. Appl. 9(4), 451–457 (2010)
Yuxing, D., Weining, L., Shuang, W.: Study of sea-sky-line detection algorithm based on Canny theory. Comput. Meas. Control. 18(3), 697–698 (2010)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821. IEEE (2014)
Acknowledgement
This work was supported in part by the National Science Foundation of China under Grant 61703181 and Grant 61525305, and in part by the Natural Science Foundation of Shanghai under Grant 17ZR1409700 and Grant 18ZR1415300.
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Rui, Z., Jingyi, L., Hengyu, L., Qixing, C. (2020). Real-Time Obstacle Detection Based on Monocular Vision for Unmanned Surface Vehicles. In: Chen, Y., Nakano, T., Lin, L., Mahfuz, M., Guo, W. (eds) Bio-inspired Information and Communication Technologies. BICT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-030-57115-3_14
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DOI: https://doi.org/10.1007/978-3-030-57115-3_14
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