{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T04:29:41Z","timestamp":1786422581256,"version":"3.56.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52372420"],"award-info":[{"award-number":["52372420"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ningbo City Key Technology Breakthrough Program of KeChuang Yongjiang 2035","award":["2024Z177"],"award-info":[{"award-number":["2024Z177"]}]},{"name":"Baima Lake Laboratory Joint Fund of the Zhejiang Provincial Natural Science Foundation of China","award":["LBMHZ25F030002"],"award-info":[{"award-number":["LBMHZ25F030002"]}]},{"name":"Offshore Wind Power Joint Fund of Guangdong Basic and Applied Basic Research Foundation","award":["2024A1515240073"],"award-info":[{"award-number":["2024A1515240073"]}]},{"name":"Scientific Research Foundation of Hangzhou City University","award":["X-202404"],"award-info":[{"award-number":["X-202404"]}]},{"name":"Zhejiang Province Key research project","award":["2025C02242,2024C01039"],"award-info":[{"award-number":["2025C02242,2024C01039"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s11760-025-04425-9","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T10:34:55Z","timestamp":1750934095000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Multi-target detection for safety monitoring in complex substation environments using YOLO-DySE"],"prefix":"10.1007","volume":"19","author":[{"given":"Jiang","family":"Junjie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Yongqi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan","family":"Anping","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalil","family":"AL-Bukhaiti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"4425_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3321465","author":"A Rabab","year":"2023","unstructured":"Rabab, A., Xiaofeng, W., Song, W.: PLGAN: Generative adversarial networks for Power-Line segmentation in aerial images [J]. IEEE transactions on image processing: A publication of the. IEEE Signal. Process. Soc. (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3321465","journal-title":"IEEE Signal. Process. Soc."},{"key":"4425_CR2","doi-asserted-by":"publisher","unstructured":"Juan, C., Lozano, A., Cardenas, et al.: Digital Substations and IEC 61850: A Primer [J]. Commun. Mag 61(6), 28\u201334. (2023). https:\/\/doi.org\/10.1109\/MCOM.001.2200568","DOI":"10.1109\/MCOM.001.2200568"},{"key":"4425_CR3","doi-asserted-by":"publisher","unstructured":"Rong, X., Zhong, C., Congying, W., et al.: Federated learning for graphic element detection with privacy preservation in multi-source substation drawings [J]. Expert Syst. Appl. 243122758. (2024) https:\/\/doi.org\/10.1016\/J.ESWA.2023.122758","DOI":"10.1016\/J.ESWA.2023.122758"},{"key":"4425_CR4","doi-asserted-by":"publisher","unstructured":"Aftab, A.M., Hussain, S., et al.: IEC 61850 based substation automation system: A survey [J]. Int. J. Electr. Power Energy Syst. 120106008\u2013120106008 (2020). https:\/\/doi.org\/10.1016\/j.ijepes.2020.106008","DOI":"10.1016\/j.ijepes.2020.106008"},{"key":"4425_CR5","doi-asserted-by":"publisher","unstructured":"Yiqing, L., Houlei, G., Weicong, G., et al.: Development of a Substation-Area backup protective relay for smart Substation[J]. IEEE Trans. Smart Grid 8(6):2544\u20132553. (2017) https:\/\/doi.org\/10.1109\/tsg.2016.2527687","DOI":"10.1109\/tsg.2016.2527687"},{"key":"4425_CR6","doi-asserted-by":"publisher","unstructured":"Rong, X., Zhong, C., Congying, W., et al.: PPFGED: Federated learning for graphic element detection with privacy preservation in multi-source substation drawings [J]. Expert Systems with Applications,2024,243122758-. https:\/\/doi.org\/10.1016\/J.ESWA.2023.122758","DOI":"10.1016\/J.ESWA.2023.122758"},{"key":"4425_CR7","doi-asserted-by":"publisher","unstructured":"Amril, N., Husam, M., Maen, T., et al.: Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis [J]. Fire 5(1):11\u201311. (2022) https:\/\/doi.org\/10.3390\/FIRE5010011","DOI":"10.3390\/FIRE5010011"},{"key":"4425_CR8","doi-asserted-by":"publisher","unstructured":"P. C, V. K. Forest fire detection on LANDSAT images using support vector machine [J]. Concurrency Computation: Pract. Experience, 33 (16). (2021). https:\/\/doi.org\/10.1002\/CPE.6280","DOI":"10.1002\/CPE.6280"},{"key":"4425_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2018.03.037","author":"H Wu","year":"2018","unstructured":"Wu, H., Zhao, J.: Comput. Ind. 100, 267\u2013277 (2018). https:\/\/doi.org\/10.1016\/j.compind.2018.03.037 An intelligent vision-based approach for helmet identification for work safety [J]"},{"key":"4425_CR10","doi-asserted-by":"publisher","unstructured":"HaeGon, J., Sunghoon, I., Byeong, et al.: A Large-scale Virtual Dataset and Egocentric Localization for Disaster Responses. [J]. IEEE transactions on pattern analysis and machine intelligence, (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3094531","DOI":"10.1109\/TPAMI.2021.3094531"},{"key":"4425_CR11","doi-asserted-by":"publisher","unstructured":"Kaushal, M., Khehra, S.B., Sharma, A.: Soft computing based object detection and tracking approaches: State-of-the-Art survey [J]. Applied Soft Computing Journal,2018,70423-464. https:\/\/doi.org\/10.1016\/j.asoc.2018.05.023","DOI":"10.1016\/j.asoc.2018.05.023"},{"key":"4425_CR12","doi-asserted-by":"publisher","unstructured":"Zhao, Z.: Enhancing Artistic analysis through deep learning: A graphic Art element recognition model based on SSD and FPT. [J]. PeerJ. Computer science,2024,10e1761-e1761. https:\/\/doi.org\/10.7717\/PEERJ-CS.1761","DOI":"10.7717\/PEERJ-CS.1761"},{"key":"4425_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, L., Li, J., Zhang, F.: An efficient forest fire target detection model based on improved YOLOv5[J]. Fire,2023,6(8). https:\/\/doi.org\/10.3390\/FIRE6080291","DOI":"10.3390\/FIRE6080291"},{"key":"4425_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Dou, Y., Yang, K., et al.: Insulator defect detection based on BaS-YOLOv5 [J]. Multimedia Syst. 2024,30(4):212\u2013212. https:\/\/doi.org\/10.1007\/s00530-024-01413-w","DOI":"10.1007\/s00530-024-01413-w"},{"issue":"1","key":"4425_CR15","doi-asserted-by":"publisher","first-page":"9362","DOI":"10.1038\/s41598-024-60126-2","volume":"14","author":"P Chen Junjie, Siqi","year":"2024","unstructured":"Chen Junjie, Siqi, P., Yanping, C., et al.: A new method based on YOLOv5 and multiscale data augmentation for visual inspection in substation [J]. Sci. Rep. 14(1), 9362\u20139362 (2024). https:\/\/doi.org\/10.1038\/s41598-024-60126-2","journal-title":"Sci. Rep."},{"key":"4425_CR16","doi-asserted-by":"publisher","unstructured":"Li, K., Jiancong, W., et al.: A fast and lightweight detection algorithm for passion fruit pests based on improved YOLOv5 [J]. Comput. Electron. Agric. 204 (2023). https:\/\/doi.org\/10.1016\/J.COMPAG.2022.107534","DOI":"10.1016\/J.COMPAG.2022.107534"},{"issue":"16","key":"4425_CR17","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.3390\/RS13163095","volume":"13","author":"Z Zhao Jianqing, Xiaohu","year":"2021","unstructured":"Zhao Jianqing, Xiaohu, Z., Jiawei, Y., et al.: A wheat Spike detection method in UAV images based on improved YOLOv5 [J]. Remote Sens. 13(16), 3095\u20133095 (2021). https:\/\/doi.org\/10.3390\/RS13163095","journal-title":"Remote Sens."},{"key":"4425_CR18","doi-asserted-by":"publisher","unstructured":"GIRSHICK, R., DONAHUE, J., DARRELL, T., Rich feature hierarchies for accurate object detection and semantic segmentation[J]. IEEE Conference on Computer, Vision, and Pattern Recognition: (2013): 580\u2013\u200987 (2014). https:\/\/doi.org\/10.48550\/arXiv.1311.2524","DOI":"10.48550\/arXiv.1311.2524"},{"key":"4425_CR19","doi-asserted-by":"publisher","unstructured":"Girshick, R., Fast R-CNN[J]: IEEE International Conference on Computer Vision (ICCV). Santiago. Chile. 1440\u20131448 (2015). https:\/\/doi.org\/10.1109\/CVPR.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"issue":"6","key":"4425_CR20","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., et al.: Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4425_CR21","doi-asserted-by":"publisher","unstructured":"Liu, W., Anguelov, D., ERHAN, D., et al.: SSD: single shot multibox detector [J]. European Conference on Computer Vision (2015). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"4425_CR22","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: Unified, real-time object detection[J]. Proceedings of the IEEE conference on computer vision and pattern recognition. 779\u2013788. (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"4425_CR23","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger[J]. Proceedings of the IEEE conference on computer vision and pattern recognition. 7263\u20137271 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"4425_CR24","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement[J]. ArXiv Preprint ArXiv:1804.02767, (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"4425_CR25","doi-asserted-by":"publisher","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection [J]. arXiv preprint arXiv:2004. https:\/\/doi.org\/10.48550\/arXiv.2004.10934","DOI":"10.48550\/arXiv.2004.10934"},{"key":"4425_CR26","doi-asserted-by":"publisher","unstructured":"Huawei, Z., Xinyu, P., Tianhao, Z., et al.: Research on flame detection method based on improved SSD algorithm [J]. J. Intell. Fuzzy Syst. 2023,45(4):6501\u20136512. https:\/\/doi.org\/10.3233\/JIFS-232645","DOI":"10.3233\/JIFS-232645"},{"key":"4425_CR27","doi-asserted-by":"publisher","unstructured":"Zhao, Z.: Enhancing Artistic analysis through deep learning: A graphic Art element recognition model based on SSD and FPT [J]. PeerJ Comput. Sci.,2024,10e1761-e1761. https:\/\/doi.org\/10.7717\/PEERJ-CS.1761","DOI":"10.7717\/PEERJ-CS.1761"},{"key":"4425_CR28","doi-asserted-by":"publisher","unstructured":"Mengdong, Z., Shuai, L., Jianjun, L.: Multi-scale forest flame detection based on improved and optimized YOLOv5 [J]. Fire Technol. 2023,59(6):3689\u20133708. https:\/\/doi.org\/10.1007\/S10694-023-01486-5","DOI":"10.1007\/S10694-023-01486-5"},{"key":"4425_CR29","doi-asserted-by":"publisher","unstructured":"Zhao, X., Wang, Q., Zhang, M., et al.: CSFF-YOLOv5: Improved YOLOv5 based on channel split and feature fusion in femoral neck fracture detection[J]. Internet of Things,2024,26101190-. https:\/\/doi.org\/10.1016\/j.iot.2024.101190","DOI":"10.1016\/j.iot.2024.101190"},{"key":"4425_CR30","doi-asserted-by":"publisher","unstructured":"Zhao, X., Wang, Q., Zhang, M., et al.: CSFF-YOLOv5: Improved YOLOv5 based on channel split and feature fusion in femoral neck fracture detection [J]. Internet of Things,2024, https:\/\/doi.org\/10.1016\/j.iot.2024.101190","DOI":"10.1016\/j.iot.2024.101190"},{"key":"4425_CR31","doi-asserted-by":"publisher","unstructured":"S. K J, S. D. An attentive convolutional transformer-based network for road safety [J]. J. Supercomputing, 79 (14): 16351\u201316377. (2023). https:\/\/doi.org\/10.1007\/s11227-023-05293-1","DOI":"10.1007\/s11227-023-05293-1"},{"key":"4425_CR32","doi-asserted-by":"publisher","unstructured":"Chen, B., Bai, D., Lin, H., et al.: Flame transnet: Advancing forest flame segmentation with fusion and augmentation Techniques[J]. Forests. 14(9) (2023). https:\/\/doi.org\/10.3390\/F14091887","DOI":"10.3390\/F14091887"},{"key":"4425_CR33","doi-asserted-by":"publisher","unstructured":"Fu, X., Zhao, S., Wang, C., et al.: Green fruit detection with a small dataset under a similar color background based on the improved YOLOv5-AT[J]. Foods. 13(7) (2024). https:\/\/doi.org\/10.3390\/FOODS13071060","DOI":"10.3390\/FOODS13071060"},{"key":"4425_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENGAPPAI.2023.106224","author":"Y Guang","year":"2023","unstructured":"Guang, Y., Chunhe, S., et al.: Bubble detection in photoresist with small samples based on GAN augmentations and modified YOLO[J]. Eng. Appl. Artif. Intell. (2023). https:\/\/doi.org\/10.1016\/J.ENGAPPAI.2023.106224","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4425_CR35","doi-asserted-by":"publisher","unstructured":"W P, L., D C B, S., et al.: Toward improved surveillance of Aedes aegypti breeding grounds through artificially augmented data[J]. Engineering Applications of Artificial Intelligence,2023,123. https:\/\/doi.org\/10.1016\/j.engappai.2023.106488","DOI":"10.1016\/j.engappai.2023.106488"},{"key":"4425_CR36","doi-asserted-by":"publisher","unstructured":"Salman, M.E., \u00c7akirsoy \u00c7akar, G., Azimjonov, J., et al.: Automated prostate cancer grading and diagnosis system using deep learning-based Yolo object detection algorithm [J]. Expert Syst. Appl. 201 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117148","DOI":"10.1016\/j.eswa.2022.117148"},{"issue":"1","key":"4425_CR37","doi-asserted-by":"publisher","first-page":"9362","DOI":"10.1038\/s41598-024-60126-2","volume":"14","author":"C Junjie","year":"2024","unstructured":"Junjie, C., Siqi, P., Yanping, C., et al.: A new method based on YOLOv5 and multiscale data augmentation for visual inspection in substation[J]. Sci. Rep. 14(1), 9362\u20139362 (2024). https:\/\/doi.org\/10.1038\/s41598-024-60126-2","journal-title":"Sci. Rep."},{"key":"4425_CR38","doi-asserted-by":"publisher","unstructured":"Kangshun, L., Jiancong, W., et al.: A fast and lightweight detection algorithm for passion fruit pests based on improved YOLOv5 [J]. Comput. Electron. Agric.,2023,204. https:\/\/doi.org\/10.1016\/J.COMPAG.2022.107534","DOI":"10.1016\/J.COMPAG.2022.107534"},{"issue":"3","key":"4425_CR39","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1504\/IJBIC.2024.137915","volume":"23","author":"J Li","year":"2024","unstructured":"Li, J., Zhao, M., Qin, Z., et al.: Detection and recognition of multiple QR codes based on YOLO_CBAM algorithm [J]. Int. J. Bio-Inspired Comput. 23(3), 179\u2013188 (2024). https:\/\/doi.org\/10.1504\/IJBIC.2024.137915","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"4425_CR40","doi-asserted-by":"publisher","unstructured":"Dong-Yan, Z., Wenhao, Z., Tao, C., et al.: Detection of wheat scab fungus spores utilizing the Yolov5-ECA-ASFF network structure [J]. Comput. Electron. Agric. 210 (2023). https:\/\/doi.org\/10.1016\/J.COMPAG.2023.107953","DOI":"10.1016\/J.COMPAG.2023.107953"},{"issue":"9","key":"4425_CR41","doi-asserted-by":"publisher","first-page":"2061","DOI":"10.3390\/Agronomy12092061","volume":"12","author":"Z JianLin","year":"2022","unstructured":"JianLin, Z., WenHao, S., HeYi, Z., et al.: SE-YOLOv5x: An optimized model based on transfer learning and visual attention mechanism for identifying and localizing weeds and vegetables [J]. Agronomy. 12(9), 2061\u20132061 (2022). https:\/\/doi.org\/10.3390\/Agronomy12092061","journal-title":"Agronomy"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04425-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04425-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04425-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T12:46:51Z","timestamp":1753188411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04425-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,26]]},"references-count":41,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["4425"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04425-9","relation":{"references":[{"id-type":"doi","id":"10.1016\/j.compind.2018.03.037","asserted-by":"subject"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,26]]},"assertion":[{"value":"17 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2025","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":"804"}}