{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:10:57Z","timestamp":1774681857227,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698905","type":"print"},{"value":"9789819698912","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-9891-2_37","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T05:46:07Z","timestamp":1753422367000},"page":"442-453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MS-DETR: Multi-Scale and Attention-Enhanced Rust Detection for Bolts and Nuts in Transmission Lines"],"prefix":"10.1007","author":[{"given":"Di","family":"Sun","sequence":"first","affiliation":[]},{"given":"Yitong","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Chaojie","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Haifeng","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Chuanlei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Alhassan, A.B., Zhang, X., Shen, H., Xu.,H.: Power transmission line inspection robots: a review, trends and challenges for future research. Int. J. Electr. Power Energy Syst. 118, 105862 (2020)","DOI":"10.1016\/j.ijepes.2020.105862"},{"key":"37_CR2","unstructured":"Eisenbeiss, H., et al.: A mini unmanned aerial vehicle (UAV): system overview and image acquisition. International Archives of Photogram metry. Remote Sensing Spatial Info. Sci. 36(5\/W1), 1-7 (2004)"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Akbari, Y., Almaadeed, N., Al-Maadeed, S., El harrouss, O.: Applications, databases and open computer vision research from drone videos and images: a survey. Artif. Intell. Rev. 54, 3887\u20133938 (2021)","DOI":"10.1007\/s10462-020-09943-1"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Carion,.N.: End-to-end object detection with transformers (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"37_CR5","unstructured":"Wang, Y., Zhang, X., Yang, T., Sun, J.: Anchor DETR: query design for transformer-based object detection (2021)"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Meng, D., et al.: Conditional DETR for fast training convergence (2021)","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"37_CR7","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)"},{"key":"37_CR8","unstructured":"Liu,.S.: Dab-DETR: dynamic anchor boxes are better queries for DETR (2022)"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Zhao,.Y.: DETRs beat YOLOs on real-time object detection (2023)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Comput. Soc. (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"37_CR11","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recog nition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Ross Girshick. Fast r-cnn. Computer Science, 2015","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"37_CR13","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., Doll\u00e1r, P.: Panoptic feature pyramid networks (2019)","DOI":"10.1109\/CVPR.2019.00656"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Yi Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 99, 2999\u20133007 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"5","key":"37_CR16","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1109\/TPAMI.2023.3342120","volume":"46","author":"K Duan","year":"2024","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet++ for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 46(5), 3509\u20133521 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR17","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Senborn, D.W., Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale (2020)"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Wang, T., Yuan, L., Chen, Y., Feng, J., Yan, S.: PNP-DETR: towards efficient visual analysis with transformers (2021)","DOI":"10.1109\/ICCV48922.2021.00462"},{"key":"37_CR19","unstructured":"Roh, B., Shin, J., Shin, W., Kim, S.: Sparse DETR: efficient end-to-end object detection with learnable sparsity. arXiv e-prints (2021)"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Yang, J., Zhang, P., Yuan, L., Zhang, L.: Dynamic DETR: end-to-end object detection with dynamic attention. In: International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00298"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Luo, P., Wang, B., Wang, H., Ma, F., Ma, H., Wang, L.: Anultrasmall bolt defect detection method for transmission line inspection. IEEE Trans. Instrum. Meas. 72, 1\u201312 (2023)","DOI":"10.1109\/TIM.2023.3241994"},{"key":"37_CR22","first-page":"1","volume":"72","author":"Z Song","year":"2023","unstructured":"Song, Z., Huang, X., Ji, C., Zhang, Y.: Deformable YOLOX: detection and rust warning method of transmission line connection fittings based on image processing technology. IEEE Trans. Instr. Measur. 72, 1\u201321 (2023)","journal-title":"IEEE Trans. Instr. Measur."},{"issue":"24","key":"37_CR23","doi-asserted-by":"publisher","first-page":"4065","DOI":"10.1049\/gtd2.13330","volume":"18","author":"G Hua","year":"2024","unstructured":"Hua, G., Zhang, H., Huang, C., Pan, M., Yan, J., Zhao, H.: An enhanced yolov8-based bolt detection algorithm for transmission line. IET Gener. Transm. Distrib. 18(24), 4065\u20134077 (2024)","journal-title":"IET Gener. Transm. Distrib."},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Qi, Y., Huo, Y., Liu, S., Jin, Y.: Opti mized yolox based transmission line bolt cascade detection. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 2099\u20132102 (2022)","DOI":"10.1109\/SMC53654.2022.9945349"},{"key":"37_CR25","first-page":"1","volume":"72","author":"K Zhang","year":"2023","unstructured":"Zhang, K., et al.: Pa-DETR: end-to end visually indistinguishable bolt defects detection method based on transmission line knowledge reasoning. IEEE Trans. Instrum. Measur. 72, 1\u201314 (2023)","journal-title":"IEEE Trans. Instrum. Measur."},{"key":"37_CR26","first-page":"07","volume":"9","author":"D Zhang","year":"2022","unstructured":"Zhang, D., Hao, X., Liang, L., Liu, W., Qin, C.: Anovel deep convolutional neural network algorithm for surface defect detection. J. Comput. Des. Eng. 9, 07 (2022)","journal-title":"J. Comput. Des. Eng."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9891-2_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:04:27Z","timestamp":1774677867000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9891-2_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698905","9789819698912"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9891-2_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}