YOLOX with CBAM for insulator detection in transmission lines
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17245-1.pdf
Reference37 articles.
1. Fan P, Shen HM, Zhao C, Wei Z, Yao JG, Zhou ZQ, Fu R, Hu Q (2021) Defect identification detection research for insulator of transmission lines based on deep learning. J Phys Conf Ser 1828:012019. https://doi.org/10.1088/1742-6596/1828/1/012019
2. Siddiqui ZA, Park U (2020) A drone based transmission line components inspection system with deep learning technique. Energies 13:3348. https://doi.org/10.3390/en13133348
3. Loukas C, Kelekis N (2022) Editorial for MRI-based multiple instance convolutional neural network (MICNN) for increased accuracy in the differentiation of borderline and malignant epithelial ovarian tumors. J Magn Reson Imaging 56:182–183. https://doi.org/10.1002/jmri.28008
4. Park J, Jun MB, Yun H (2022) Development of robotic bin picking platform with cluttered objects using human guidance and convolutional neural network (CNN). J Manuf Syst 63:539–549. https://doi.org/10.1016/j.jmsy.2022.05.011
5. Rahman S, Ramli M, Arnia F, Muharar R, Sembiring A (2021) Performance analysis of mAlexnet by training option and activation function tuning on parking images. IOP Conf Ser: Mater Sci Eng 1087:012084. https://doi.org/10.1088/1757-899X/1087/1/012084
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