[1] Cheng H, Yu J Y, Dai Y N et al. Overview and prospect for technologies oil and gas pipeline inspection robot[J]. Journal of Wuhan Institute of Technology, 43, 324-333(2021).

[2] Lin L L, Guo Y, Liu J et al. Application of ultrasonic guided wave technology in the pipelines corrosion detection[J]. Petro & Chemical Equipment, 24, 125-127, 130(2021).

[3] Niu G L, Sun X Y, Li X C et al. Research on eddy current non-destructive testing system of internal surface of oil and gas pipeline[J]. Instrument Technique and Sensor, 86-89, 126(2021).

[4] Chen A J, Li J Z, Li D D. Improved randomized algorithm for circle detection[J]. Opto-Electronic Engineering, 33, 91-95(2006).

[5] Ma B B, Li Y, Neng C X. The detection of inner-pipeline obstruction with laser projection photograph method[J]. Opto-Electronic Engineering, 40, 113-117(2013).

[6] Ma G. Current status and development trend of in-line inspection of oil & gas pipeline[J]. Petrochemical Safety and Environmental Protection Technology, 37, 6, 26-29(2021).

[7] Hou W F. Comprehensive application of nondestructive testing technology in pressure pipeline inspection[J]. Modern Industrial Economy and Informationization, 11, 142-144(2021).

[8] Dang N, Wang W B, Zhang T et al. 3D simulation and experiment of pulsed eddy current testing for pipeline corrosion[J]. Nondestructive Testing, 39, 57-60, 74(2017).

[9] Wang H M, Han Z G, Chen L H. A multi-resolution image fusion algorithm without reconstruction[J]. Opto-Electronic Engineering, 38, 86-92(2011).

[10] Wang J G, Lin C J, Chen S M. Applying fuzzy method to vision-based lane detection and departure warning system[J]. Expert Systems With Applications, 37, 113-126(2010).

[11] Tang G, Deng X S, Wang Q Y. Research on point cloud filtering algorithm based on density clustering[J]. Laser & Optoelectronics Progress, 59, 1628003(2022).

[12] Liu H X, Chen W H, Liu S Y. Multi-frame point cloud fusion algorithm based on IMU and dynamic target detection[J]. Application Research of Computers, 38, 2179-2182(2021).

[13] Wang W Q, Li Z C, Fu Y J et al. Multi-factor segmentation of point cloud based on improved multi-rule region growing[J]. Acta Optica Sinica, 41, 0528001(2021).

[14] Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum, 26, 214-226(2007).

[15] Tran T T, Cao V T, Laurendeau D. Extraction of cylinders and estimation of their parameters from point clouds[J]. Computers & Graphics, 46, 345-357(2015).

[16] Lei X D, Wang H T, Zhao Z Z. Small-sample airborne LiDAR point cloud classification based on transfer learning and fully convolutional network[J]. Chinese Journal of Lasers, 48, 1610001(2021).

[17] Flores-Sintas A, Cadenas J M, Martin F. Detecting homogeneous groups in clustering using the Euclidean distance[J]. Fuzzy Sets and Systems, 120, 213-225(2001).

[18] Cao X, Lin Z X, Song S L et al. Multispectral LiDAR point cloud denoising based on color clustering[J]. Laser & Optoelectronics Progress, 58, 1228002(2021).