[1] Park K C, Motai Y, Yoon J R. Acoustic fault detection technique for high-power insulators[J]. IEEE Transactions on Industrial Electronics, 64, 9699-9708(2017).
[2] Lei X S, Sui Z H. Intelligent fault detection of high voltage line based on the Faster R-CNN[J]. Measurement, 138, 379-385(2019).
[3] Liang H G, Zuo C, Wei W M. Detection and evaluation method of transmission line defects based on deep learning[J]. IEEE Access, 8, 38448-38458(2020).
[4] Miao X R, Liu X Y, Chen J et al. Insulator detection in aerial images for transmission line inspection using single shot multibox detector[J]. IEEE Access, 7, 9945-9956(2019).
[5] Zhang X Y, An J B, Chen F M. A simple method of tempered glass insulator recognition from airborne image[C], 127-130(2010).
[6] Tan P, Li X F, Xu J M et al. Catenary insulator defect detection based on contour features and gray similarity matching[J]. Journal of Zhejiang University-SCIENCE A, 21, 64-73(2020).
[7] Wu Q G, An J B. An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 3613-3626(2014).
[8] Tao X, Zhang D P, Wang Z H et al. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50, 1486-1498(2020).
[9] Wang B F, Zhao H T. Small object detection in hyperspectral images based on radial basis activation function[J]. Acta Optica Sinica, 41, 2311001(2021).
[10] Liang X, Li J W, Zhao X L et al. Infrared target imaging liquid level detection method based on deep learning[J]. Acta Optica Sinica, 41, 2110001(2021).
[11] Hu J, Liu H, Xu W C et al. Position detection algorithm of road obstacles based on 3D LiDAR[J]. Chinese Journal of Lasers, 48, 2410001(2021).
[12] Jiang H, Qiu X J, Chen J et al. Insulator fault detection in aerial images based on ensemble learning with multi-level perception[J]. IEEE Access, 7, 61797-61810(2019).
[13] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).
[15] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).
[16] Liu J J, Liu C Y, Wu Y Q et al. An improved method based on deep learning for insulator fault detection in diverse aerial images[J]. Energies, 14, 4365(2021).
[18] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 37, 1904-1916(2015).
[19] Wu X W, Sahoo D, Hoi S C H. Recent advances in deep learning for object detection[J]. Neurocomputing, 396, 39-64(2020).
[20] Law H, Deng J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 128, 642-656(2020).
[21] Zhou X Y, Zhuo J C, Krähenbühl P. Bottom-up object detection by grouping extreme and center points[C], 850-859(2019).
[22] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[EB/OL]. https://arxiv.org/abs/1904.07850
[23] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[24] Bao W X, Yang Y P, Liang D et al. Multi-residual module stacked hourglass networks for human pose estimation[J]. Journal of Beijing Institute of Technology (English Edition), 29, 110-119(2020).
[25] Feng Z Y, Jin L W, Tao D P et al. DLANet: a manifold-learning-based discriminative feature learning network for scene classification[J]. Neurocomputing, 157, 11-21(2015).
[26] Gao S H, Cheng M M, Zhao K et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).
[27] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).
[28] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).
[29] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).