[1] [1] CHEN G C, GU G M, YU X N, et al. Method for defect detection of vatenary insulators by combining the features of PHOG and BOW-SURF[J]. Journal of Chinese Computer Systems, 2021,42(1): 172-177.

[2] [2] QIN W N, WEN H R, LI F. Fabric defect detection algorithm based on residual energy distribution and Gabor feature fusion[J]. The Visual Computer, 2022, 33: 1-15.

[3] [3] LI S B, YANG J, WANG Z, et al. Review of development and application of defect detection technology[J]. Acta Automatica Sinica, 2020,46(11): 2319-2336.

[4] [4] ANAMI B S, ELEMMI M C. Comparative analysis of SVM and ANN classifiers for defective and non-defective fabric images classification[J]. The Journal of the Textile Institute, 2022,113(6): 1072-1082.

[5] [5] KIM H，DAVID O，KSENIA K，et al. Evaluation of a computer vision-based system to analyse behavioral changes in high school classrooms[J]. International Journal of Information and Communication Technology Education，2021，17（4）：1-12.

[6] [6] SINGH K，YADAV D K. Adaptive background modelling technique for moving object detection in video under dynamic environment[J]. International Journal of Spatio-Temporal Data Science，2019，1（1）：4-21.

[7] [7] SONG Y，CHAO M，WU X，et al. VITAL：visual tracking via adversarial learning[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE，2018: 8990-8999.

[8] [8] SONG X N, CHEN Y M, FENG Z H, et al. Collaborative representation based face classification exploiting block weighted LBP and analysis dictionary learning[J]. Pattern Recognition, 2019,88: 127-138.

[9] [9] TRUONG H P, KIM Y G. Enhanced Line Local Binary Patterns (EL-LBP): An efficient image representation for face recognition[C]//International Conference on Advanced Concepts for Intelligent Vision Systems, September 24-28, Poitiers, France. Cham: Springer, 2018: 285-296.

[10] [10] JANG H, KIM T Y, LEE Y C, et al. A review: technological trends and development direction of pipeline robot systems[J]. Journal of Intelligent & Robotic Systems, 2022,105(3): 1-20.

[11] [11] HU H, ZHANG K, TAN A H, et al. A sim-to-real pipeline for deep reinforcement learning for autonomous robot navigation in cluttered rough terrain[J]. IEEE Robotics and Automation Letters, 2021, 6(4): 6569-6576.

[12] [12] OLIMOY S S, MAMUROVA D I. Graphic Information processing technology and its importance[J]. European Journal of Life Safety and Stability, 2021,10: 1-4.

[13] [13] AMIRFAKHRIAN M, PARHIZKAR M. Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents[J]. Journal of Big Data, 2021,8(1): 1-17.

[14] [14] MORU D K, BORRO D. A machine vision algorithm for quality control inspection of gears[J]. International Journal of Advanced Manufacturing Technology, 2020,106(2): 16-19.

[15] [15] KIEN B H, IBA D, TSUTSUI Y, et al. Effect of image size on performance of a plastic gear crack detection system based convolutional neural networks: an experimental study[C]//Health Monitoring of Structural and Biological Systems IX, April 23-25, 2020, Online. Bellingham: SPIE, 2020: 113812I.

[16] [16] WANG L, YE W, SHAO Y, et al. A new adaptive evolutionary digital filter based on alternately evolutionary rules for fault detection of gear tooth spalling[J]. Mechanical Systems and Signal Processing, 2018,118: 645-657.

[17] [17] XIAO M, WANG W, SHEN X, et al. Research on defect detection method of powder metallurgy gear based on machine vision[J]. Machine Vision and Applications, 2021, 32(2): 12-16.

[18] [18] ZHENG H, CHENG G, LI Y, et al. A new fault diagnosis method for planetary gear based on image feature extraction and bag-of-words model[J]. Measurement, 2019,145: 1-13.