References
[1] Wang H, Zheng H, Tian J, et al. Research on quantitative identification method for wire rope wire breakage damage signals based on multi-decomposition information fusion. J Saf Sustain. 2024;1(2):89-97.
[2] Chen Y, Qin W, Wang Q, et al. Influence of corrosion pit on the tensile mechanical properties of a multi-layered wire rope strand. Constr Build Mater. 2021;302:124387.
[3] Jiang X, Sun Y, Feng B, et al. New on-line MFL testing method and apparatus for winding mine hoist wire rope. Appl Sci. 2022;12(14):6970.
[4] Tian J, Bai Q, Wang X, et al. Experimental study on characterization of mine wire rope detection signal properties based on magnetic field model. Coal Sci Technol. 2024;52(2):279-91.
[5] Rossteutscher I, Blaschke O, Dötzer F, et al. Improved EMAT sensor design for enhanced ultrasonic signal detection in steel wire ropes. Sensors. 2024;24(22):7114.
[6] Zhao S, Li G, Wang C. Bridge cable damage identification based on acoustic emission technology: a comprehensive review. Measurement. 2024:115195.
[7] Liu Q, Tang Q, Su B, et al. Wire rope damage detection based on a uniform-complementary binary pattern with exponentially weighted guide image filtering. Vis Comput. 2024:1-14.
[8] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Com-puter Vision and Pattern Recognition (CVPR'05). IEEE; 2005. p. 886-93.
[9] Liu Q, Wang C, Li Y, et al. A fabric defect detection method based on deep learning. IEEE Access. 2022;10:4284-96.
[10] Zhou C, Lu Z, Lv Z, et al. Metal surface defect detection based on improved YOLOv5. Sci Rep. 2023;13(1):20803.
[11] Hu J, Wan W, Qiao P, et al. Power insulator defect detection method based on enhanced YOLOV7 for aerial inspection. Electronics. 2025;14(3):408.
[12] Wu T, Dong Y. YOLO-SE: improved YOLOv8 for remote sensing object detection and recognition. Appl Sci. 2023;13(24):12977.
[13] Misra D, Nalamada T, Arasanipalai AU, et al. Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 2021. p. 3139-48.
[14] Tong Z, Chen Y, Xu Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv:2301.10051. 2023.
[15] Zhou P, Zhou G, Wang H, et al. Intelligent visual detection method for the early surface damage of mine hoisting wire ropes. Meas Sci Technol. 2024;35(11):115018.
[16] Lin T, Goyal P, Girshick R, et al. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision; 2017. p. 2980-8.
[17] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. In: Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I 14. Springer International Publishing; 2016. p. 21-37.
[18] Redmon J, Farhadi A. Yolov3: an incremental improvement. arXiv:1804.02767. 2018.
[19] Li C, Li L, Jiang H, et al. YOLOv6: a single-stage object detection framework for industrial applications. arXiv:2209.02976. 2022.
[20] Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2023. p. 7464-75.
[21] Khanam R, Hussain M. Yolov11: an overview of the key architectural enhancements. arXiv:2410.17725. 2024.
[22] Tian Y, Ye Q, Doermann D. Yolov12: attention-centric real-time object detectors. arXiv:2502.12524. 2025.