{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:09:24Z","timestamp":1770840564531,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Natural Science Foundation of Anhui Universities","award":["KJ2019A0106"],"award-info":[{"award-number":["KJ2019A0106"]}]},{"name":"the Natural Science Foundation of Anhui Universities","award":["KJ2019A0106"],"award-info":[{"award-number":["KJ2019A0106"]}]},{"name":"the Natural Science Foundation of Anhui Universities","award":["KJ2019A0106"],"award-info":[{"award-number":["KJ2019A0106"]}]},{"name":"2020 Anhui Education Department Project","award":["2020JYXM0460"],"award-info":[{"award-number":["2020JYXM0460"]}]},{"name":"2020 Anhui Education Department Project","award":["2020JYXM0460"],"award-info":[{"award-number":["2020JYXM0460"]}]},{"name":"2020 Anhui Education Department Project","award":["2020JYXM0460"],"award-info":[{"award-number":["2020JYXM0460"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s11554-025-01737-4","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T12:59:22Z","timestamp":1753189162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FGDA-DETR: a lightweight and real-time enhanced algorithm for wire rope defect detection with improved accuracy and efficiency"],"prefix":"10.1007","volume":"22","author":[{"given":"Xin","family":"Li","sequence":"first","affiliation":[]},{"given":"Qianqian","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Wenqing","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"issue":"4","key":"1737_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s10921-020-00732-y","volume":"39","author":"S Liu","year":"2020","unstructured":"Liu, S., Sun, Y., Jiang, X., Kang, Y.: A review of wire rope detection methods, sensors and signal processing techniques. J. Nondestruct. Eval. 39(4), 85 (2020). https:\/\/doi.org\/10.1007\/s10921-020-00732-y","journal-title":"J. Nondestruct. Eval."},{"issue":"6","key":"1737_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/su15065441","volume":"15","author":"P Mazurek","year":"2023","unstructured":"Mazurek, P.: A comprehensive review of steel wire rope degradation mechanisms and recent damage detection methods. Sustainability 15(6), 5441 (2023)","journal-title":"Sustainability"},{"key":"1737_CR3","doi-asserted-by":"publisher","unstructured":"Sun, B., Yang, J., Li, B., Li, S., Wang, L., Xu, Z.: Wire rope inspection robots: a review. J. Shanghai Jiaotong Univ. (Sci.) (2023).https:\/\/doi.org\/10.1007\/s12204-023-2641-8","DOI":"10.1007\/s12204-023-2641-8"},{"issue":"15","key":"1737_CR4","doi-asserted-by":"publisher","first-page":"8297","DOI":"10.1109\/JSEN.2020.2970070","volume":"20","author":"P Zhou","year":"2020","unstructured":"Zhou, P., Zhou, G., Li, Y., He, Z., Liu, Y.: A hybrid data-driven method for wire rope surface defect detection. IEEE Sens. J. 20(15), 8297\u20138306 (2020)","journal-title":"IEEE Sens. J."},{"issue":"12","key":"1737_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/app13127069","volume":"13","author":"M Wang","year":"2023","unstructured":"Wang, M., Li, J., Xue, Y.: A new defect diagnosis method for wire rope based on CNN-Transformer and transfer learning. Appl. Sci. 13(12), 7069 (2023)","journal-title":"Appl. Sci."},{"issue":"9","key":"1737_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/app14093753","volume":"14","author":"J Han","year":"2024","unstructured":"Han, J., Zhang, Y., Feng, Z., Zhao, L.: Research on intelligent identification algorithm for steel wire rope damage based on residual network. Appl. Sci. 14(9), 3753 (2024)","journal-title":"Appl. Sci."},{"issue":"7","key":"1737_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/e26070531","volume":"26","author":"P Li","year":"2024","unstructured":"Li, P., Tian, J.: Research on internal damage identification of wire rope based on improved VGG network. Entropy 26(7), 531 (2024)","journal-title":"Entropy"},{"issue":"2","key":"1737_CR8","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1177\/1475921719855915","volume":"19","author":"J Rostami","year":"2019","unstructured":"Rostami, J., Tse, P.W., Yuan, M.: Detection of broken wires in elevator wire ropes with ultrasonic guided waves and tone-burst wavelet. Struct. Health Monit. 19(2), 481\u2013494 (2019). https:\/\/doi.org\/10.1177\/1475921719855915","journal-title":"Struct. Health Monit."},{"issue":"1","key":"1737_CR9","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2184\/1\/012035","volume":"2184","author":"F Pan","year":"2022","unstructured":"Pan, F., Ren, L., Zhou, J., Liu, Z.: Fault classification based on computer vision for steel wire ropes. J. Phys. Conf. Ser. 2184(1), 012035 (2022). https:\/\/doi.org\/10.1088\/1742-6596\/2184\/1\/012035","journal-title":"J. Phys. Conf. Ser."},{"issue":"2","key":"1737_CR10","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s40684-021-00343-6","volume":"9","author":"Z Ren","year":"2022","unstructured":"Ren, Z., Fang, F., Yan, N., Wu, Y.: State of the art in defect detection based on machine vision. Int. J. Precis. Eng. Manuf.-Green Technol. 9(2), 661\u2013691 (2022). https:\/\/doi.org\/10.1007\/s40684-021-00343-6","journal-title":"Int. J. Precis. Eng. Manuf.-Green Technol."},{"key":"1737_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2021.102405","volume":"119","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Zhang, E., Yan, X.: Quantitative method for detecting internal and surface defects in wire rope. NDT E Int. 119, 102405 (2021)","journal-title":"NDT E Int."},{"issue":"5","key":"1737_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/s21051769","volume":"21","author":"L Shi","year":"2021","unstructured":"Shi, L., Tan, J., Xue, S., Deng, J.: Inspection method of rope arrangement in the ultra-deep mine hoist based on optical projection and machine vision. Sensors 21(5), 1769 (2021)","journal-title":"Sensors"},{"key":"1737_CR13","doi-asserted-by":"crossref","unstructured":"Li, W., Dong, T., Shi, H., Ye, L.: Defect detection algorithm of wire rope based on color segmentation and Faster RCNN. In: 2021 International Conference on Control, Automation and Information Sciences (ICCAIS) (2021). https:\/\/ieeexplore.ieee.org\/abstract\/document\/9624670","DOI":"10.1109\/ICCAIS52680.2021.9624670"},{"issue":"1","key":"1737_CR14","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2872\/1\/012013","volume":"2872","author":"E Shi","year":"2024","unstructured":"Shi, E., Huang, X., Zhang, P., Hu, G.: Research on surface defect detection method of wire rope outer covering layer based on deep learning. J. Phys. Conf. Ser. 2872(1), 012013 (2024). https:\/\/doi.org\/10.1088\/1742-6596\/2872\/1\/012013","journal-title":"J. Phys. Conf. Ser."},{"issue":"23","key":"1737_CR15","doi-asserted-by":"publisher","first-page":"22985","DOI":"10.1109\/JSEN.2022.3214109","volume":"22","author":"P Zhou","year":"2022","unstructured":"Zhou, P., Zhou, G., Wang, S., Wang, H., He, Z., Yan, X.: Visual sensing inspection for the surface damage of steel wire ropes with object detection method. IEEE Sens. J. 22(23), 22985\u201322993 (2022)","journal-title":"IEEE Sens. J."},{"key":"1737_CR16","doi-asserted-by":"crossref","unstructured":"Medaramatla, S.C., Samhitha, C.V.S.R.K.: Detection of damages in wire ropes using computer vision techniques. In: 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (2024). https:\/\/ieeexplore.ieee.org\/document\/10548850","DOI":"10.1109\/ICDCECE60827.2024.10548850"},{"key":"1737_CR17","doi-asserted-by":"publisher","unstructured":"Wang, C., Dai, H., Liu, Q.: SPSS-YOLO: An Improved Steel Wire Rope Damage Detection Model Based on YOLOv8. PREPRINT (Version 1) available at Research Square, 24 April 2025. https:\/\/doi.org\/10.21203\/rs.3.rs-6402577\/v1","DOI":"10.21203\/rs.3.rs-6402577\/v1"},{"key":"1737_CR18","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, H., Liu, Q.: TW-YOLO: high-precision steel wire rope detection algorithm based on triplet attention. Adv. Comput. Commun. 6(2) (2025). https:\/\/www.hillpublisher.com\/ArticleDetails\/4722#fu","DOI":"10.26855\/acc.2025.04.001"},{"key":"1737_CR19","first-page":"1","volume":"73","author":"J Shen","year":"2024","unstructured":"Shen, J., Liu, N., Sun, H., Li, D., Zhang, Y.: An instrument indication acquisition algorithm based on lightweight deep convolutional neural network and hybrid attention fine-grained features. IEEE Trans. Instrum. Meas. 73, 1\u201316 (2024)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"1737_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging10120309","volume":"10","author":"J Li","year":"2024","unstructured":"Li, J., Wang, J., Kong, D., Zhang, Q., Qiang, Z.: FastQAFPN-YOLOv8s-based method for rapid and lightweight detection of walnut unseparated material. J. Imaging 10(12), 309 (2024)","journal-title":"J. Imaging"},{"issue":"1","key":"1737_CR21","doi-asserted-by":"publisher","first-page":"7649","DOI":"10.1038\/s41598-025-92148-9","volume":"15","author":"J Hong","year":"2025","unstructured":"Hong, J., Ye, K., Qiu, S.: Study on lightweight strategies for L-YOLO algorithm in road object detection. Sci. Rep. 15(1), 7649 (2025). https:\/\/doi.org\/10.1038\/s41598-025-92148-9","journal-title":"Sci. Rep."},{"issue":"1","key":"1737_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s40494-025-01565-6","volume":"13","author":"J Shen","year":"2025","unstructured":"Shen, J., Liu, N., Sun, H., Li, D., Zhang, Y., Han, L.: An algorithm based on lightweight semantic features for ancient mural element object detection. NPJ Herit. Sci. 13(1), 70 (2025). https:\/\/doi.org\/10.1038\/s40494-025-01565-6","journal-title":"NPJ Herit. Sci."},{"key":"1737_CR23","first-page":"1","volume":"71","author":"J Shen","year":"2022","unstructured":"Shen, J., Liu, N., Xu, C., Sun, H., Xiao, Y., Li, D., Zhang, Y.: Finger vein recognition algorithm based on lightweight deep convolutional neural network. IEEE Trans. Instrum. Meas. 71, 1\u201313 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"1737_CR24","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: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024). https:\/\/ieeexplore.ieee.org\/document\/10657220","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"1737_CR25","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't walk: chasing higher FLOPS for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023). https:\/\/ieeexplore.ieee.org\/document\/10203371","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"1737_CR26","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, Y., Ge, Y., Zhao, S., Song, L., Yue, X., Shan, Y.: UniRepLKNet: a universal perception large-Kernel ConvNet for audio video point cloud time-series and image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2024). https:\/\/ieeexplore.ieee.org\/document\/10655060","DOI":"10.1109\/CVPR52733.2024.00527"},{"key":"1737_CR27","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020). https:\/\/ieeexplore.ieee.org\/document\/9157333","DOI":"10.1109\/CVPR42600.2020.00165"},{"issue":"11","key":"1737_CR28","doi-asserted-by":"publisher","first-page":"8210","DOI":"10.1109\/TNNLS.2022.3144163","volume":"34","author":"H Tan","year":"2023","unstructured":"Tan, H., Liu, X., Yin, B., Li, X.: MHSA-Net: multihead self-attention network for occluded person re-identification. IEEE Trans. Neural Netw. Learn. Syst. 34(11), 8210\u20138224 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1737_CR29","unstructured":"Zhang, T., Li, L., Zhou, Y., Liu, W., Qian, C., Ji, X.: CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications. arXiv:2024.abs\/2408.03703"},{"key":"1737_CR30","doi-asserted-by":"crossref","unstructured":"Adarsh, P., Rathi, P., Kumar, M.: YOLO v3-Tiny: object detection and recognition using one stage improved model. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (2020). https:\/\/ieeexplore.ieee.org\/document\/9074315.","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"1737_CR31","doi-asserted-by":"crossref","unstructured":"Varghese, R.: YOLOv8: a novel object detection algorithm with enhanced performance and robustness. In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) (2024). https:\/\/ieeexplore.ieee.org\/document\/10533619","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"1737_CR32","unstructured":"Khanam, R., Hussain, M.: What is YOLOv5: a deep look into the internal features of the popular object detector (2024). arXiv:2407.20892"},{"key":"1737_CR33","unstructured":"Khanam, R., Hussain, M.: Yolov11: an overview of the key architectural enhancements (2024). arXiv:2410.17725"},{"key":"1737_CR34","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., Mark Liao, H.-Y.: Yolov9: learning what you want to learn using programmable gradient information. In: European Conference on Computer Vision. Springer (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"1737_CR35","first-page":"107984","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J.: 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":"1737_CR36","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: deformable transformers for end-to-end object detection (2020). arXiv:2010.04159"},{"key":"1737_CR37","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 (2022). arXiv:2203.03605"},{"issue":"6","key":"1737_CR38","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":"1737_CR39","doi-asserted-by":"publisher","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: Single Shot MultiBox Detector. In: Computer Vision\u2014ECCV 2016. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01737-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01737-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01737-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T16:59:37Z","timestamp":1757350777000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01737-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["1737"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01737-4","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,22]]},"assertion":[{"value":"9 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":3,"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":"Conflict of interest"}}],"article-number":"150"}}