DEVELOPMENT OF METHODS OF REMOVING DEFECTS IN IMAGES OF POWER FACILITIES OBTAINED IN THE PROCESS OF REMOTE MONITORING USING UAV
DOI:
https://doi.org/10.26906/SUNZ.2018.4.008Keywords:
overhead power lines, remote monitoring, UAV, line transfer, image processing, median filtering, crosscorrelation functionAbstract
The purpose of the article is to develop and experimentally study methods of artifacts removal on images obtained as a result of remote monitoring of power facilities by UAVs caused by line-by-line transfer of an image from the camera to the storage device. During the capturing of a frame, the objects change position, which is due to the nature of the UAV's movement, and as a result, parts of a frame display different points in time. Such a delay in obtaining data from the camera relative to motion in the frame leads to the appearance of well-noticeable geometric distortions of objects. Cameras with line-by-line transfer are low cost, thus, their use significantly reduce the cost of the monitoring system, but requires methods for processing the obtained images that would minimize geometric distortions known as rolling shutter effect. During the research, methods of correlation estimation of the relative shift of sequences representing image lines, and digital nonlinear filtering were used. Mathematical modeling was carried out in the Scilab package. Methods for suppressing the rolling shutter effect, which do not require analysis of the sequence of frames, have been proposed and implemented in software. Thus, it makes possible to eliminate distortions caused by both the inclination of the video camera and its vibration. This does not require an assessment of the camera frequency or selection of certain areas in an image. Proposed methods can be implemented in for UAV on-board computer that can eliminate artifacts on images acquired during monitoring in real time. This enables to simplify the automatic selection of contours and objects on an image, as well as the estimation of quantitative and qualitative characteristics of objects based on the results of photo and video recording. Obtained the calculated relationships, which allow determining the expected values of the line shift of an image caused by the rolling shutter effect. In addition to images in the visible spectrum, the proposed methods can be used to process thermal images. The analyses show that the proposed methods can almost completely eliminate image artifacts caused by the rolling shutter effect.Downloads
References
Skarbek L., Zak A., Ambroziak D. Damage detection strategies in structural health monitoring of overhead power transmission system. – 7th European Workshop on Structural Health Monitoring. Clerk Maxwell, A Treatise on Electricity and Magnetism. 2014. 3rd ed., vol. 2. pp. 68–73. HAL Id : hal-01020412
Li L. The UAV intelligent inspection of transmission lines. – International Conference on Advances in Mechanical Engineering and Industrial Informatics. 2015. pp. 1542–1545. doi: 10.2991/ameii-15.2015.285
Adabo G. J. Unmanned aircraft system for high voltage power transmission lines of Brazilian electrical system. – Journal of Power and Energy Engineering. February 2014. 8(2). pp. 394–398. doi: 10.1049/oap-cired.2017.1048
Sheinin M., Schechner Y., Kutulakos N. Rolling shutter imaging on the electric grid. – IEEE International Conference on Computational Photography (ICCP). 2018. pp.1–12. doi: 10.1109/ICCPHOT.2018.8368472
Chun J.-B., Jung H., Kyung, C.-M. Suppressing rolling-shutter distortion of CMOS image sensors by motion vector detection. – IEEE Transactions on Consumer Electronics. 2008. Vol. 54, Is. 4. pp. 1479–1487. doi: 10.1109/TCE.2008.4711190
Baker S., Bennett E., Kang S., Szeliski, R. Removing rolling shutter wobble. – IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2010. pp. 2392–2399. doi: 10.1109/CVPR.2010.5539932 Системи управління, навігації та зв'язку, 2018, випуск 4(50) ISSN 2073-7394 12
Rengarajan V., Rajagopalan A., Aravind R., Seetharaman G. Image Registration and Change Detection under Rolling Shutter Motion Blur. – IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017. Volume: 39, Issue: 10, pp. 1959– 1972. doi: 10.1109/TPAMI.2016.2630687
Punnappurath A., Rengarajan V., Rajagopalan A. Rolling Shutter Super-Resolution. – IEEE International Conference on Computer Vision (ICCV). 2015. pp. 558–566. doi: 10.1109/ICCV.2015.71
Ali A., Rasha E., Alser T. Median Filter Performance Based on Different Window Sizes for Salt and Pepper Noise Removal in Gray and RGB Images. – International Journal of Signal Processing, Image Processing and Pattern Recognition. 2015. Vol.8, No.10, pp.343–352. doi: 10.14257/ijsip.2015.8.10.34
