{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:06:11Z","timestamp":1771653971949,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Cooperation project between Lanzhou University of Technology and Longnan Power Supply Company of State Grid Gansu Electric Power Company","award":["HX2023C5080000"],"award-info":[{"award-number":["HX2023C5080000"]}]},{"name":"Lanzhou University of Technology Higher Education Research Project","award":["202102001"],"award-info":[{"award-number":["202102001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11760-026-05109-8","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:51:27Z","timestamp":1769043087000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparse and asymmetric perception transformer for lightweight insulator defect detection in complex scenes"],"prefix":"10.1007","volume":"20","author":[{"given":"Changsheng","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Lulu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Hongwei","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yongxin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"5109_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.105862","volume":"118","author":"AB Alhassan","year":"2020","unstructured":"Alhassan, A.B., Zhang, X., Shen, H., Xu, H.: Power transmission line inspection robots: A review, trends and challenges for future research. International Journal of Electrical Power & Energy Systems 118, 105862 (2020)","journal-title":"International Journal of Electrical Power & Energy Systems"},{"issue":"2","key":"5109_CR2","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TPWRD.2024.3353203","volume":"39","author":"N Jain","year":"2024","unstructured":"Jain, N., Bedi, J., Anand, A., Godara, S.: A transfer learning architecture to detect faulty insulators in powerlines. IEEE Trans. Power Delivery 39(2), 1002\u20131011 (2024)","journal-title":"IEEE Trans. Power Delivery"},{"issue":"9","key":"5109_CR3","doi-asserted-by":"publisher","first-page":"12051","DOI":"10.1007\/s11042-016-3981-2","volume":"76","author":"Y Zhai","year":"2017","unstructured":"Zhai, Y., Wang, D., Zhang, M., Wang, J., Guo, F.: Fault detection of insulator based on saliency and adaptive morphology. Multimedia Tools and Applications 76(9), 12051\u201312064 (2017)","journal-title":"Multimedia Tools and Applications"},{"key":"5109_CR4","doi-asserted-by":"publisher","first-page":"35316","DOI":"10.1109\/ACCESS.2018.2846293","volume":"6","author":"Y Zhai","year":"2018","unstructured":"Zhai, Y., Chen, R., Yang, Q., Li, X., Zhao, Z.: Insulator fault detection based on spatial morphological features of aerial images. IEEE Access 6, 35316\u201335326 (2018)","journal-title":"IEEE Access"},{"issue":"4","key":"5109_CR5","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TDEI.2016.7556487","volume":"23","author":"L Cheng","year":"2016","unstructured":"Cheng, L., Wang, L., Mei, H., Guan, Z., Zhang, F.: Research of nondestructive methods to test defects hidden within composite insulators based on thz time-domain spectroscopy technology. IEEE Trans. Dielectr. Electr. Insul. 23(4), 2126\u20132133 (2016)","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"5109_CR6","first-page":"1","volume":"70","author":"W Zhao","year":"2021","unstructured":"Zhao, W., Xu, M., Cheng, X., Zhao, Z.: An insulator in transmission lines recognition and fault detection model based on improved faster rcnn. IEEE Trans. Instrum. Meas. 70, 1\u20138 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"5109_CR7","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1049\/stg2.12135","volume":"7","author":"Y Li","year":"2024","unstructured":"Li, Y., Xu, Y., Sun, W., Qian, Q., Li, Z., Jiang, X.: Ecc-rcnn: An efficient and high-accuracy object detection framework for transmission line defect identification. IET Smart Grid 7(1), 28\u201337 (2024)","journal-title":"IET Smart Grid"},{"key":"5109_CR8","doi-asserted-by":"crossref","unstructured":"Cai, P., Jiang, P., Liu, Y.: Yolov11-pc: an intelligent detection method for concrete structure defects. Measurement Science and Technology (2025)","DOI":"10.1088\/1361-6501\/ade466"},{"key":"5109_CR9","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Liu, D.: Adin-detr: Adapting detection transformer for end-to-end real-time power line insulator defect detection. IEEE Transactions on Instrumentation and Measurement (2024)","DOI":"10.1109\/TIM.2024.3420265"},{"key":"5109_CR10","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"5109_CR11","doi-asserted-by":"publisher","unstructured":"Jocher, G.: YOLOv5 by Ultralytics. https:\/\/github.com\/ultralytics\/yolov5. Version 7.0 (2020). https:\/\/doi.org\/10.5281\/zenodo.3908559","DOI":"10.5281\/zenodo.3908559"},{"key":"5109_CR12","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO. https:\/\/github.com\/ultralytics\/ultralytics. Version 8.0.0 (2023)"},{"key":"5109_CR13","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., Ding, G., Du, S., Wu, Z., Gao, Y.: Yolov13: Real-time object detection with hypergraph-enhanced adaptive visual perception. arXiv:2506.17733 (2025)"},{"key":"5109_CR14","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, 213\u2013229 (2020). Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"5109_CR15","doi-asserted-by":"crossref","unstructured":"Huang, S., Lu, Z., Cun, X., Yu, Y., Zhou, X., Shen, X.: Deim: Detr with improved matching for fast convergence. In: Proceedings of the Computer Vision and Pattern Recognition Conference, 15162\u201315171 (2025)","DOI":"10.1109\/CVPR52734.2025.01412"},{"key":"5109_CR16","unstructured":"Peng, Y., Li, H., Wu, P., Zhang, Y., Sun, X., Wu, F.: D-fine: Redefine regression task in detrs as fine-grained distribution refinement. arXiv:2410.13842 (2024)"},{"key":"5109_CR17","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16965\u201316974 (2024)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"5109_CR18","doi-asserted-by":"crossref","unstructured":"Li, J., Zhou, H., Lv, G., Chen, J.: A2mada-yolo: Attention alignment multiscale adversarial domain adaptation yolo for insulator defect detection in generalized foggy scenario. IEEE Transactions on Instrumentation and Measurement (2025)","DOI":"10.1109\/TIM.2025.3541814"},{"key":"5109_CR19","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H., Chan, S.-H.G.: Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 12021\u201312031 (2023)","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"5109_CR20","unstructured":"Lee, D., Yun, S., Ro, Y.: Emulating self-attention with convolution for efficient image super-resolution. arXiv:2503.06671 (2025)"},{"key":"5109_CR21","first-page":"1140","volume":"35","author":"M-H Guo","year":"2022","unstructured":"Guo, M.-H., Lu, C.-Z., Hou, Q., Liu, Z., Cheng, M.-M., Hu, S.-M.: Segnext: Rethinking convolutional attention design for semantic segmentation. Adv. Neural. Inf. Process. Syst. 35, 1140\u20131156 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5109_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., Zhang, L.: Efficient long-range attention network for image super-resolution. In: European Conference on Computer Vision, 649\u2013667 (2022). Springer","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"5109_CR23","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"5109_CR24","doi-asserted-by":"crossref","unstructured":"Yang, J., Liu, S., Wu, J., Su, X., Hai, N., Huang, X.: Pinwheel-shaped convolution and scale-based dynamic loss for infrared small target detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, 39, 9202\u20139210 (2025)","DOI":"10.1609\/aaai.v39i9.32996"},{"key":"5109_CR25","unstructured":"project: INSULATOR FAULTS DETECTION Dataset. Roboflow. visited on 2025-12-08 (2023). https:\/\/universe.roboflow.com\/project-vmgqx\/insulator-faults-detection"},{"key":"5109_CR26","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European Conference on Computer Vision, 21\u201337 (2016). Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5109_CR27","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., Wang, C., et al Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 14454\u201314463 (2021)","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"5109_CR28","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., et al.: Yolov6: A single-stage object detection framework for industrial applications. arXiv:2209.02976 (2022)"},{"key":"5109_CR29","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., Mark\u00a0Liao, H.-Y.: Yolov9: Learning what you want to learn using programmable gradient information. In: European Conference on Computer Vision, 1\u201321 (2024). Springer","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"5109_CR30","first-page":"107984","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., et al.: Yolov10: Real-time end-to-end object detection. Adv. Neural. Inf. Process. Syst. 37, 107984\u2013108011 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5109_CR31","unstructured":"Tian, Y., Ye, Q., Doermann, D.: Yolov12: Attention-centric real-time object detectors. arXiv preprint arXiv:2502.12524 (2025)"},{"key":"5109_CR32","unstructured":"Wang, Z., Li, C., Xu, H., Zhu, X.: Mamba yolo: Ssms-based yolo for object detection. arXiv e-prints, 2406 (2024)"},{"key":"5109_CR33","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, X., Fan, Z., Zeng, G., Li, H., Yuan, Y., Sun, L., Wang, J.: Conditional detr for fast training convergence. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 3651\u20133660 (2021)","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"5109_CR34","doi-asserted-by":"crossref","unstructured":"Li, F., Zhang, H., Liu, S., Guo, J., Ni, L.M., Zhang, L.: Dn-detr: Accelerate detr training by introducing query denoising. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 13619\u201313627 (2022)","DOI":"10.1109\/CVPR52688.2022.01325"},{"key":"5109_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, X., Yang, T., Sun, J.: Anchor detr: Query design for transformer-based detector. In: Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2567\u20132575 (2022)","DOI":"10.1609\/aaai.v36i3.20158"},{"key":"5109_CR36","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.-Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv:2203.03605 (2022)"},{"key":"5109_CR37","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05109-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05109-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05109-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:26:19Z","timestamp":1771651579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05109-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,22]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["5109"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05109-8","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,22]]},"assertion":[{"value":"24 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"58"}}