Abstract
Perception of the environment is the prerequisite for the realization of unmanned driving. Correctly detecting the lane line and navigating the vehicle is the key technology in unmanned driving. This paper mainly aims at the low accuracy of the traditional lane detection algorithm in the complex environment such as night and rain, and proposes a lane detection and recognition method based on dynamic region of interest (ROI) selection and firefly algorithm. First, perform distortion correction on the captured lane image, gray scale and blur image preprocessing, and then determine the height of the ROI based on the vanishing point, and dynamically adjust the width of the ROI based on the recognition of the lane line in the previous frame to achieve dynamic ROI adjust to eliminate interference factors and reduce the amount of calculation to the greatest extent. Finally, to solve the problem that the canny operator is sensitive to noise in the traditional method, an improved firefly algorithm is proposed for edge detection. The slope-limited progressive probability Hough transform is used to detect the straight line of the ROI divided into several boxes, and the least square method is used to fit several detected straight lines to extract lane lines. Experimental results show that the method we proposed can achieve lane line detection well in complex environments, with an average accuracy rate of 96.37%, and an average detection time per frame of only 118 ms.
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Barua, B., Biswas, S., Deb, K.: An efficient method of lane detection and tracking for highway safety[C]// 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), pp. 1–6. IEEE, Khada (2019)
Chen, S., Zhang, S., Shang, J., et al.: Brain-inspired cognitive model with attention for self-driving cars[J]. IEEE Trans. Cogn. Dev. Syst. 11(1), 13–25 (2017)
Häne, C., Heng, L., Lee, G.H., et al.: 3D visual perception for self-driving cars using a multi-camera system: calibration, mapping, localization, and obstacle detection[J]. Image Vis. Comput. 68, 14–27 (2017)
Hecht, J.: Lidar for self-driving cars[J]. Opt. Photonics News 29(1), 26–33 (2018)
Kong, H., Sarma, S.E., Tang, F.: Generalizing Laplacian of Gaussian filters for vanishing-point detection[J]. IEEE Trans. Intell. Transp. Syst. 14(1), 408–418 (2012)
Malmir, S., Shalchian, M.: Design and FPGA implementation of dual-stage lane detection, based on Hough transform and localized stripe features[J]. Microprocess. Microsyst. 64, 12–22 (2019)
Matas, J., Galambos, C., Kittler, J.: Robust detection of lines using the progressive probabilistic hough transform [J]. Comput. Vis. Image Underst. 78(1), 119–137 (2000)
Muthalagu, R., Bolimera, A., Kalaichelvi, V.: Lane detection technique based on perspective transformation and histogram analysis for self-driving cars[J]. Comput. Electr. Eng. 85, 106653 (2020)
Paden, B., Čáp, M., Yong, S.Z., et al.: A survey of motion planning and control techniques for self-driving urban vehicles[J]. IEEE Trans. Intell. Veh. 1(1), 33–55 (2016)
Pizzati, F., Allodi, M., Barrera, A., et al.: Lane detection and classification using cascaded CNNs[C]//International conference on computer aided systems theory, pp. 95–103. Springer, Cham (2019)
Rong, W., Li, Z., Zhang, W., et al. An improved CANNY edge detection algorithm[C]//International conference on mechatronics and automation, pp. 577–582. IEEE, Tianjin (2014)
Stilgoe, J.: Machine learning, social learning and the governance of self-driving cars[J]. Soc. Stud. Sci. 48(1), 25–56 (2018)
Wientapper, F., Wuest, H., Rojtberg, P., et al. A camera-based calibration for automotive augmented reality Head-Up-Displays[C]//International Symposium on Mixed and Augmented Reality (ISMAR), pp. 10–21. IEEE, Adelaide (2013)
Yang, X.S.: Firefly algorithms for multimodal optimization[C]. In: International Symposium on Stochastic Algorithms, pp. 169–178. Springer, Berlin (2009)
Yoo, J.H., et al.: A robust lane detection method based on vanishing point estimation using the relevance of line segments[J]. IEEE Trans. Intell. Transp. Syst 18, 3254–3266 (2017)
Zhang, Z.: A flexible new technique for camera calibration[J]. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
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Shen, Y., Bi, Y., Yang, Z. et al. Lane line detection and recognition based on dynamic ROI and modified firefly algorithm. Int J Intell Robot Appl 5, 143–155 (2021). https://doi.org/10.1007/s41315-021-00175-2
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DOI: https://doi.org/10.1007/s41315-021-00175-2