{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:40:57Z","timestamp":1769521257206,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation Innovation and Development Joint Fund Project of Chongqing","award":["CSTB2023NSCQ-LZX0148"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0148"]}]},{"name":"Natural Science Foundation Innovation and Development Joint Fund Project of Chongqing","award":["CSTB2023NSCQ-LZX0148"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0148"]}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2023NSCQ-MSX0407"],"award-info":[{"award-number":["CSTB2023NSCQ-MSX0407"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2023NSCQ-MSX0407"],"award-info":[{"award-number":["CSTB2023NSCQ-MSX0407"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302338"],"award-info":[{"award-number":["62302338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04861-7","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T01:35:05Z","timestamp":1761010505000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["YOLOv8-ORE: An Efficient Ore Segmentation Network based on Adaptive Feature Extraction and Attention-Enhanced Spatial Fusion"],"prefix":"10.1007","volume":"19","author":[{"given":"Yidan","family":"Long","sequence":"first","affiliation":[]},{"given":"Baoning","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Jianming","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wenfeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qibing","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"4861_CR1","doi-asserted-by":"publisher","first-page":"5987","DOI":"10.1007\/s11760-024-03286-y","volume":"18","author":"HH Zhou","year":"2024","unstructured":"Zhou, H.H., Cai, G.P., Luo, H.: Research on segmentation and reconstruction of overlapping ore contours based on eam-solov2 and convex hulls. Signal, Image and Video Processing 18, 5987\u20135995 (2024). https:\/\/doi.org\/10.1007\/s11760-024-03286-y","journal-title":"Signal, Image and Video Processing"},{"key":"4861_CR2","doi-asserted-by":"publisher","first-page":"9152","DOI":"10.1109\/TII.2024.3379670","volume":"20","author":"GD Sun","year":"2024","unstructured":"Sun, G.D., Peng, Y.T., Cheng, L., Xu, M.Y., Wang, A., Wu, B.: An efficient mlp-based point-guided segmentation network for ore images with ambiguous boundary. IEEE Transactions on Industrial Informatics 20, 9152\u20139162 (2024). https:\/\/doi.org\/10.1109\/TII.2024.3379670","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"4861_CR3","doi-asserted-by":"publisher","first-page":"431","DOI":"10.3390\/sym11030431","volume":"11","author":"YT Zhan","year":"2019","unstructured":"Zhan, Y.T., Zhang, G.Y.: An improved otsu algorithm using histogram accumulation moment for ore segmentation. Symmetry 11, 431 (2019). https:\/\/doi.org\/10.3390\/sym11030431","journal-title":"Symmetry"},{"key":"4861_CR4","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s11831-019-09323-1","volume":"27","author":"T Serkan","year":"2020","unstructured":"Serkan, T., Hamzeh, Z., Ali, T., Moghadas, N.F., Ali, K., Burak, S.: A brief review and a new automatic method for interpretation of polypropylene modified bitumen based on fuzzy radon transform and watershed segmentation. Archives of Computational Methods in Engineering 27, 773\u2013803 (2020). https:\/\/doi.org\/10.1007\/s11831-019-09323-1","journal-title":"Archives of Computational Methods in Engineering"},{"issue":"7","key":"4861_CR5","doi-asserted-by":"publisher","first-page":"11722","DOI":"10.1109\/JSEN.2025.3543918","volume":"25","author":"F Li","year":"2025","unstructured":"Li, F., Liu, X., Li, Z.: A two-stage framework with ore-detect and segment anything model for ore particle segmentation and size measurement. IEEE Sens. J. 25(7), 11722\u201311736 (2025). https:\/\/doi.org\/10.1109\/JSEN.2025.3543918","journal-title":"IEEE Sens. J."},{"key":"4861_CR6","doi-asserted-by":"publisher","first-page":"59048","DOI":"10.1109\/ACCESS.2021.3072998","volume":"9","author":"Z Yang","year":"2021","unstructured":"Yang, Z., Ding, H., Guo, L., Lian, M.: Superpixel image segmentation-based particle size distribution analysis of fragmented rock. IEEE Access 9, 59048\u201359058 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3072998","journal-title":"IEEE Access"},{"key":"4861_CR7","doi-asserted-by":"crossref","unstructured":"Yuan, L., Duan, Y.Y.: A method of ore image segmentation based on deep learning (2018). Paper presented at the Intelligent Computing Methodologies, 15-18 (2018)","DOI":"10.1007\/978-3-319-95957-3_53"},{"key":"4861_CR8","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Wang, X.L., Chen, H.: Boxinst: High-performance instance segmentation with box annotations. Paper presented at the IEEE\/CVF conference on computer vision and pattern recognition, 5443\u20135452 2021 (2021)","DOI":"10.1109\/CVPR46437.2021.00540"},{"issue":"8","key":"4861_CR9","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.3390\/s21082615","volume":"21","author":"W Wang","year":"2021","unstructured":"Wang, W., Li, Q., Xiao, C.Y., Zhang, D.Z., Miao, L., Wang, L.: An improved boundary-aware u-net for ore image semantic segmentation. Sensors 21(8), 2615 (2021). https:\/\/doi.org\/10.3390\/s21082615","journal-title":"Sensors"},{"issue":"6","key":"4861_CR10","doi-asserted-by":"publisher","first-page":"4725","DOI":"10.1007\/s00500-023-09131-7","volume":"28","author":"W Wang","year":"2024","unstructured":"Wang, W., Li, Q., Chen, P., Zhang, D.Z., Xiao, C.Y., Wang, Z.H.: An improved u-net based network for multiclass segmentation and category ratio statistics of ore images. Soft. Comput. 28(6), 4725\u20134741 (2024). https:\/\/doi.org\/10.1007\/s00500-023-09131-7","journal-title":"Soft. Comput."},{"key":"4861_CR11","doi-asserted-by":"publisher","first-page":"5526","DOI":"10.3390\/min12050526","volume":"12","author":"GD Sun","year":"2022","unstructured":"Sun, G.D., Huang, D.L., Cheng, L., Jia, J.J., Xiong, C.Y., Zhang, Y.: Efficient and lightweight framework for real-time ore image segmentation based on deep learning. Minerals 12, 5526 (2022). https:\/\/doi.org\/10.3390\/min12050526","journal-title":"Minerals"},{"issue":"5","key":"4861_CR12","doi-asserted-by":"publisher","first-page":"474","DOI":"10.3390\/diagnostics14050474","volume":"14","author":"A Sahafi","year":"2024","unstructured":"Sahafi, A., Koulaouzidis, A., Lalinia, M.: Polypoid lesion segmentation using yolo-v8 network in wireless video capsule endoscopy images. Diagnostics 14(5), 474 (2024). https:\/\/doi.org\/10.3390\/diagnostics14050474","journal-title":"Diagnostics"},{"issue":"11","key":"4861_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-025-04456-2","volume":"19","author":"X Li","year":"2025","unstructured":"Li, X., Wang, P., Wang, J., Zeng, F., Liu, X.: Gda-yolov8n: a lightweight small object detection algorithm based on gradual feature aggregation. SIViP 19(11), 1\u201312 (2025). https:\/\/doi.org\/10.1007\/s11760-025-04456-2","journal-title":"SIViP"},{"key":"4861_CR14","doi-asserted-by":"crossref","unstructured":"Chen, H., Tao, R., Zhang, H., Wang, Y.D., Li, X., Ye, W., Wang, J.D., Hu, G.S., Savvides, M.: Conv-adapter: Exploring parameter efficient transfer learning for convnets (2024). Paper presented at the IEEE\/CVF conference on computer vision and pattern recognition, 1551\u20131561 (2024)","DOI":"10.1109\/CVPRW63382.2024.00162"},{"key":"4861_CR15","unstructured":"Zhang, X., Song, Y.Z., Song, T.T., Yang, D.G., Ye, Y.C., Zhou, J., Zhang, L.M.: Akconv: Convolutional kernel with arbitrary sampled shapes and arbitrary number of parameter. arXiv preprint arXiv: 2311.1158 (2023)"},{"issue":"17","key":"4861_CR16","doi-asserted-by":"publisher","first-page":"5640","DOI":"10.3390\/s24175640","volume":"24","author":"XL Zhang","year":"2024","unstructured":"Zhang, X.L., Nie, J.L., Wei, S.L., Zhu, G.F., Dai, W., Yang, C.: A study of classroom behavior recognition incorporating super-resolution and target detection. Sensors 24(17), 5640 (2024). https:\/\/doi.org\/10.3390\/s24175640","journal-title":"Sensors"},{"issue":"17","key":"4861_CR17","doi-asserted-by":"publisher","first-page":"73354","DOI":"10.1109\/ACCESS.2024.3404867","volume":"12","author":"XR Qin","year":"2024","unstructured":"Qin, X.R., Yu, C.D., Liu, B.S., Zhang, Z.H.: Yolo8-fasg: a high-accuracy fish identification method for underwater robotic system. IEEE Access 12(17), 73354\u201373362 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3404867","journal-title":"IEEE Access"},{"key":"4861_CR18","unstructured":"Glenn, J.: Yolov5 by ultralytics (2020). https:\/\/github.com\/ultralytics\/yolov5"},{"issue":"11","key":"4861_CR19","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.3390\/s24113647","volume":"24","author":"MX Liu","year":"2024","unstructured":"Liu, M.X., Li, R.X., Hou, M.X., Zhang, C., Hu, J.M., Wu, Y.J.: Sd-yolov8: an accurate seriola dumerili detection model based on improved yolov8. Sensors 24(11), 3647 (2024). https:\/\/doi.org\/10.3390\/s24113647","journal-title":"Sensors"},{"key":"4861_CR20","doi-asserted-by":"publisher","first-page":"145853","DOI":"10.1109\/ACCESS.2023.3345889","volume":"11","author":"LY Shen","year":"2023","unstructured":"Shen, L.Y., Lang, B.H., Song, Z.X.: Infrared object detection method based on dbd-yolov8. IEEE Access 11, 145853\u2013145868 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3345889","journal-title":"IEEE Access"},{"key":"4861_CR21","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.3390\/ani141420899","volume":"14","author":"ZH Li","year":"2024","unstructured":"Li, Z.H., Luo, S.L., Xiang, J., Chen, Y.Q., Luo, Q.H.: Improved chinese giant salamander parental care behavior detection based on yolov8. Animals 14, 2089 (2024). https:\/\/doi.org\/10.3390\/ani141420899","journal-title":"Animals"},{"issue":"24","key":"4861_CR22","doi-asserted-by":"publisher","DOI":"10.3390\/app1324129777","volume":"13","author":"TY Wu","year":"2023","unstructured":"Wu, T.Y., Dong, Y.K.: Yolo-se: improved yolov8 for remote sensing object detection and recognition. Appl. Sci. 13(24), 312977 (2023). https:\/\/doi.org\/10.3390\/app1324129777","journal-title":"Appl. Sci."},{"key":"4861_CR23","doi-asserted-by":"publisher","unstructured":"Wang, C.Y., Liao, H.Y.M., Yeh, I.H.: Designing network design strategies through gradient path analysis. arXiv preprint arXiv:2211.04800 (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.04800","DOI":"10.48550\/arXiv.2211.04800"},{"key":"4861_CR24","doi-asserted-by":"publisher","unstructured":"Wang, A., Chen, H., Liu, L.H., Chen, K., Lin, Z.J., Han, J.G., Ding, G.G.: Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.14458","DOI":"10.48550\/arXiv.2405.14458"},{"key":"4861_CR25","doi-asserted-by":"publisher","first-page":"1221117","DOI":"10.1016\/j.eswa.2023.122111","volume":"238","author":"Q Gao","year":"2024","unstructured":"Gao, Q., Long, T., Zhou, Z.B.: Mineral identification based on natural feature-oriented image processing and multi-label image classification. Expert Systems with Applications 238, 1221117 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.122111","journal-title":"Expert Systems with Applications"},{"key":"4861_CR26","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K.M., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection (2017). Paper presented at the IEEE conference on computer vision and pattern recognition, 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"4861_CR27","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H.F., Shi, J.P., Jia, J.Y.: Path aggregation network for instance segmentation (2018). Paper presented at the IEEE conference on computer vision and pattern recognition, 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"4861_CR28","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., Le, Q.V.: Nas-fpn: Learning scalable feature pyramid architecture for object detection (2019). Paper presented at the IEEE\/CVF conference on computer vision and pattern recognition, 87036\u20137045 (2019)","DOI":"10.1109\/CVPR.2019.00720"},{"key":"4861_CR29","doi-asserted-by":"crossref","unstructured":"Tan, M.X., Pang, R.M., Le, Q.V.: Efficientdet: Scalable and efficient object detection (2020). Paper presented at the IEEE\/CVF conference on computer vision and pattern recognition, 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"4861_CR30","doi-asserted-by":"crossref","unstructured":"He, K.M., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. Paper presented at the IEEE international conference on computer vision. 2961\u20132969, 2017 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"4861_CR31","doi-asserted-by":"crossref","unstructured":"Huang, Z.J., Huang, L.C., Gong, Y.C., Huang, C., Wang, X.G.: Mask scoring r-cnn. Paper presented at the IEEE\/CVF conference on computer vision and pattern recognition. 6409\u20136418, 2019 (2019)","DOI":"10.1109\/CVPR.2019.00657"},{"key":"4861_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X.L., Kong, T., Shen, C.H., Jiang, Y.N., Li, L.: Solo: segmenting objects by locations. Paper presented at the Computer Vision-ECCV. 649\u2013665, 2020 (2020)","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"4861_CR33","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y.X., He, K.M., Girshick, R.: Pointrend: image segmentation as rendering. Paper presented at the IEEE\/CVF conference on computer vision and pattern recognition. 9799\u20139808, 2020 (2020)","DOI":"10.1109\/CVPR42600.2020.00982"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04861-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04861-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04861-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T12:13:54Z","timestamp":1763900034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04861-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":33,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4861"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04861-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]},"assertion":[{"value":"7 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 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":"1280"}}