Abstract
Micro Aerial Vehicles (MAV)s have been distinguished, in the last decade, for their potential to inspect infrastructures in an active manner and provide critical information to the asset owners. Inspired by this trend, the mining industry is lately focusing to incorporate MAVs in their production cycles. Towards this direction, this article proposes a novel method to enhance 3D reconstruction of low-light environments, like underground tunnels, by using image processing. More specifically, the main idea is to enhance the low light resolution of the collected images, captured onboard an aerial platform, before inserting them to the reconstruction pipeline. The proposed method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm that limits the noise, while amplifies the contrast of the image. The overall efficiency and improvement achieved of the novel architecture has been extensively and successfully evaluated by utilizing data sets captured from real scale underground tunnels using a quadrotor.
This work has received funding from the European Unions Horizon 2020 Research and Innovation Program under the Grant Agreement No. 730302, SIMS.
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Attard, L., Debono, C.J., Valentino, G., Castro, M.D.: Tunnel inspection using photogrammetric techniques and image processing: a review. ISPRS J. Photogramm. Remote Sens. 144, 180–188 (2018). https://doi.org/10.1016/j.isprsjprs.2018.07.010. http://www.sciencedirect.com/science/article/pii/S0924271618302028
Schmid, K., Lutz, P., Tomić, T., Mair, E., Hirschmüller, H.: Autonomous vision-based micro air vehicle for indoor and outdoor navigation. J. Field Robot. 31(4), 537–570 (2014)
Gohl, P., Burri, M., Omari, S., Rehder, J., Nikolic, J., Achtelik, M., Siegwart, R.: Towards autonomous mine inspection. In: 2014 3rd International Conference on Applied Robotics for the Power Industry (CARPI), pp. 1–6. IEEE (2014)
Özaslan, T., Loianno, G., Keller, J., Taylor, C.J., Kumar, V., Wozencraft, J.M., Hood, T.: Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs. IEEE Robot. Autom. Lett. 2(3), 1740–1747 (2017)
Karpowicz, J.: UAVs in civil infrastructure. http://www.expouav.com/wp-content/uploads/2016/04/free-report-uavs-in-civil-infrastructure.pdf?_ga=2.149425910.1944537166.1536777582-1263854739.1536777582
Lal, S., Narasimhadhan, A., Kumar, R.: Automatic method for contrast enhancement of natural color images. J. Electr. Eng. Technol. 10(3), 1233–1243 (2015)
Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. arXiv preprint arXiv:1805.01934 (2018)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson, London (2018)
Pizer, S.M., Zimmerman, J.B., Staab, E.V.: Adaptive grey level assignment in CT scan display. J. Comput. Assist. Tomogr. 8(2), 300–305 (1984)
Zimmerman, J.B., Pizer, S.M., Staab, E.V., Perry, J.R., McCartney, W., Brenton, B.C.: An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Trans. Med. Imaging 7(4), 304–312 (1988)
Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)
Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
Nistér, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 756–770 (2004)
Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment—a modern synthesis. In: International Workshop on Vision Algorithms, pp. 298–372. Springer (1999)
Gao, X.S., Hou, X.R., Tang, J., Cheng, H.F.: Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 930–943 (2003)
Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. (ToG) 32(3), 29 (2013)
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Kanellakis, C., Karvelis, P., Nikolakopoulos, G. (2020). On Image Based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_23
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DOI: https://doi.org/10.1007/978-3-030-17798-0_23
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