{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:44:29Z","timestamp":1781196269647,"version":"3.54.1"},"reference-count":59,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T00:00:00Z","timestamp":1694649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M680883"],"award-info":[{"award-number":["2020M680883"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.<\/jats:p>","DOI":"10.3390\/e25091333","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T02:54:13Z","timestamp":1694746453000},"page":"1333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines"],"prefix":"10.3390","volume":"25","author":[{"given":"Shuxia","family":"Yan","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiachen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaohua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anhai","family":"Ai","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TIFS.2016.2542061","article-title":"Masking transmission line outages via false data injection attacks","volume":"11","author":"Liu","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TIM.2016.2556920","article-title":"A method for accurate transmission line impedance parameter estimation","volume":"65","author":"Ritzmann","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/LED.2015.2425792","article-title":"Multiring circular transmission line model for ultralow contact resistivity extraction","volume":"36","author":"Yu","year":"2015","journal-title":"IEEE Electron Device Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TPWRS.2015.2412682","article-title":"Optimal power flow with the consideration of flexible transmission line impedance","volume":"31","author":"Ding","year":"2015","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_5","first-page":"1","article-title":"An ultrasmall bolt defect detection method for transmission line inspection","volume":"72","author":"Luo","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Su, T., and Liu, D. (2023). Transmission line defect detection based on feature enhancement. Multimed. Tools Appl., 1\u201313.","DOI":"10.1007\/s11042-023-15063-z"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"38448","DOI":"10.1109\/ACCESS.2020.2974798","article-title":"Detection and evaluation method of transmission line defects based on deep learning","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.3233\/JIFS-189353","article-title":"Component identification and defect detection in transmission lines based on deep learning","volume":"40","author":"Zheng","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"117682","DOI":"10.1016\/j.eswa.2022.117682","article-title":"SLMS-SSD: Improving the balance of semantic and spatial information in object detection","volume":"206","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_10","unstructured":"Lee, N., Ajanthan, T., and Torr, P.H.S. (2018). Snip: Single-shot network pruning based on connection sensitivity. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2014ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_12","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108724","DOI":"10.1016\/j.compeleceng.2023.108724","article-title":"MCANet: Multi-channel attention network with multi-color space encoder for underwater image classification","volume":"108","author":"Li","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, A., Jia, L., Wang, J., and Wang, C. (2023). SAR Image Classification Using Gated Channel Attention Based Convolutional Neural Network. Remote Sens., 15.","DOI":"10.3390\/rs15020362"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, H., Shi, C., Wang, L., and Jin, Z. (2023). A Cross-Channel Dense Connection and Multi-Scale Dual Aggregated Attention Network for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15092367"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1109\/TCSVT.2022.3218735","article-title":"Dual Wavelet Attention Networks for Image Classification","volume":"33","author":"Yang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","first-page":"1","article-title":"Double attention based on graph attention network for image multi-label classification","volume":"19","author":"Zhou","year":"2023","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106442","DOI":"10.1016\/j.engappai.2023.106442","article-title":"Mixed local channel attention for object detection","volume":"123","author":"Wan","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ma, C., Zhuo, L., and Li, J. (2023). Arbitrary-Oriented Object Detection in Aerial Images with Dynamic Deformable Convolution and Self-Normalizing Channel Attention. Electronics, 12.","DOI":"10.3390\/electronics12092132"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2350178","DOI":"10.1142\/S0218126623501785","article-title":"An Efficient Channel Attention-Enhanced Lightweight Neural Network Model for Metal Surface Defect Detection","volume":"32","author":"Xie","year":"2022","journal-title":"J. Circuit Syst. Comp."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Su, S., Chen, R., Fang, X., and Zhang, T. (2023). A Novel Transformer-Based Adaptive Object Detection Method. Electronics, 12.","DOI":"10.3390\/electronics12030478"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"162403","DOI":"10.1007\/s11432-022-3592-8","article-title":"Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection","volume":"66","author":"Xiang","year":"2023","journal-title":"Sci. China Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ortiz, A., Robinson, C., Morris, D., Fuentes, O., Kiekintveld, C., Hassan, M.M., and Jojic, N. (2020, January 13\u201319). Local context normalization: Revisiting local normalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01129"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, X., and He, K. (2021, January 20\u201325). Exploring simple siamese representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TIM.2011.2159322","article-title":"Fault detection on transmission lines using a microphone array and an infrared thermal imaging camera","volume":"61","author":"Ha","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, M.-G., Jeong, S., Kim, S.-T., and Oh, K.-Y. (2023). Anomaly Detection of Underground Transmission-Line through Multiscale Mask DCNN and Image Strengthening. Mathematics, 11.","DOI":"10.3390\/math11143143"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1049\/smt2.12138","article-title":"Applicability of the acoustic\u2013electrical joint detection method to identify defects in gas insulated system","volume":"17","author":"Sun","year":"2023","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_28","first-page":"174","article-title":"On-line analysis of fault events in power transmission systems using SOE, fuzzy logic and expert systems","volume":"36","author":"Llano","year":"2013","journal-title":"Rev. T\u00e9cnica De La Fac. De Ing. Univ. Del Zulia"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.sna.2018.02.033","article-title":"On-line monitoring system of 35 kV 3-core submarine power cable based on \u03c6-OTDR","volume":"273","author":"Lv","year":"2018","journal-title":"Sens. Actuators A Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6080","DOI":"10.1109\/TIM.2020.2969057","article-title":"Detection method based on automatic visual shape clustering for pin-missing defect in transmission lines","volume":"69","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Instrum. Measurement."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106726","DOI":"10.1016\/j.ijepes.2020.106726","article-title":"Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques","volume":"128","author":"Manninen","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108277","DOI":"10.1016\/j.ijepes.2022.108277","article-title":"Key target and defect detection of high-voltage power transmission lines with deep learning","volume":"142","author":"Liu","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"236","DOI":"10.23919\/JSEE.2023.000010","article-title":"Bug localization based on syntactical and semantic information of source code","volume":"34","author":"Yan","year":"2023","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Okpala, I., Rodriguez, G.R., Tapia, A., Halse, S., and Kropczynski, J. (2023). A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing. arXiv.","DOI":"10.1145\/3582768.3582789"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, L., Zhang, Y., Wang, Y., Yang, H., and Tan, S. (2023). Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping. Appl. Sci., 13.","DOI":"10.3390\/app13063576"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103796","DOI":"10.1016\/j.jvcir.2023.103796","article-title":"Boosting semantic segmentation via feature enhancement","volume":"92","author":"Liu","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_37","first-page":"1509","article-title":"Semantic segmentation on remote sensing images with multi-scale feature fusion","volume":"31","author":"Jing","year":"2019","journal-title":"J. Comput.-Aided Des. Comput. Graph."},{"key":"ref_38","first-page":"81","article-title":"Improved YOLOv3 target detection based on boundary limit point features","volume":"43","author":"Li","year":"2023","journal-title":"J. Comput. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"116919","DOI":"10.1016\/j.image.2023.116919","article-title":"Enhanced semantic feature pyramid network for small object detection","volume":"113","author":"Chen","year":"2023","journal-title":"Signal Process. Image Commun."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","article-title":"Lung pattern classification for interstitial lung diseases using a deep convolutional neural network","volume":"35","author":"Anthimopoulos","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characterristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_42","unstructured":"\u0130rsoy, O., and Alpayd\u0131n, E. (2018). Continuously constructive deep neural networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Boyd, A., Czajka, A., and Bowyer, K. (2019, January 23\u201326). Deep learning-based feature extraction in iris recognition: Use existing models, fine-tune or train from scratch?. Proceedings of the 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), Tampa, FL, USA.","DOI":"10.1109\/BTAS46853.2019.9185978"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, T., Gao, P., Dong, H., Zhuang, Y., Wang, G., Zhang, W., and Chen, H. (2022). Consecutive Pre-Training: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain. Remote Sens., 14.","DOI":"10.3390\/rs14225675"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15593","DOI":"10.1007\/s11042-019-07821-9","article-title":"Transfer learning with pre-trained deep convolutional neural networks for serous cell classification","volume":"79","author":"Baykal","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Gao, Z., Xie, J., Wang, Q., and Li, P. (2019, January 15\u201320). Global second-order pooling convolutional networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00314"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (2019, January 27\u201328). Gcnet: Non-local networks meet squeeze-excitation networks and beyond. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref_50","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., and Feng, J. (2018). A2-nets: Double attention networks. Adv. Neural Inf. Process. Syst., 31."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_53","first-page":"1","article-title":"FINet: An insulator dataset and detection benchmark based on synthetic fog and improved YOLOv5","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Mea."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_55","first-page":"1","article-title":"An insulator in transmission lines recognition and fault detection model based on improved faster RCNN","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Song, J., Qian, J., Liu, Z., Jiao, Y., Zhou, J., Li, Y., Chen, Y., Guo, J., and Wang, Z. (2023). Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology. Remote Sens., 15.","DOI":"10.3390\/rs15102533"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tang, C., Dong, H., Huang, Y., Han, T., Fang, M., and Fu, J. (2023). Foreign object detection for transmission lines based on Swin Transformer V2 and YOLOX. Vis. Comput., 1\u201319.","DOI":"10.1007\/s00371-023-03004-8"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhao, L., Liu, C., and Qu, H. (2022). Transmission line object detection method based on contextual information enhancement and joint heterogeneous representation. Sensors, 22.","DOI":"10.3390\/s22186855"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6219","DOI":"10.1016\/j.egyr.2023.05.235","article-title":"Recognition of bird nests on transmission lines based on YOLOv5 and DETR using small samples","volume":"9","author":"Yang","year":"2023","journal-title":"Energy Rep."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/9\/1333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:50:58Z","timestamp":1760129458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/9\/1333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,14]]},"references-count":59,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["e25091333"],"URL":"https:\/\/doi.org\/10.3390\/e25091333","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,14]]}}}