{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T20:15:44Z","timestamp":1777666544444,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819666027","type":"print"},{"value":"9789819666034","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"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":[[2026]]},"DOI":"10.1007\/978-981-96-6603-4_2","type":"book-chapter","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T11:14:14Z","timestamp":1751973254000},"page":"17-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SMSF-Net: A Semantics-Driven Multiscale Selective Fusion Network for\u00a0Object Detection in\u00a0Remote Sensing Images"],"prefix":"10.1007","author":[{"given":"Ran","family":"Tao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailun","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Cai, X., Lai, Q., Wang, Y., Wang, W., Sun, Z., Yao, Y.: Poly Kernel Inception Network for Remote Sensing Detection. arXiv preprint arXiv:2403.06258 (2024)","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Chen, S., et al.: Info-FPN: an informative feature pyramid network for object detection in remote sensing images. Expert Syst. Appl. 214 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.119132","DOI":"10.1016\/j.eswa.2022.119132"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., Barnard, K.: Attentional feature fusion. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3560\u20133569 (2021)","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Gao, T., Liu, Z., Zhang, J., Wu, G., Chen, T.: A task-balanced multiscale adaptive fusion network for object detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. (2023)","DOI":"10.1109\/TGRS.2023.3289878"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tgrs.2023.3294241","volume":"61","author":"T Gao","year":"2023","unstructured":"Gao, T., Niu, Q., Zhang, J., Chen, T., Mei, S., Jubair, A.: Global to local: a scale-aware network for remote sensing object detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023). https:\/\/doi.org\/10.1109\/tgrs.2023.3294241","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"3032","DOI":"10.1109\/JSTARS.2020.3000317","volume":"13","author":"M Haroon","year":"2020","unstructured":"Haroon, M., Shahzad, M., Fraz, M.M.: Multisized object detection using spaceborne optical imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 3032\u20133046 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Jocher, G.: YOLOv5 by Ultralytics (2020). https:\/\/doi.org\/10.5281\/zenodo.3908559, https:\/\/github.com\/ultralytics\/yolov5","DOI":"10.5281\/zenodo.3908559"},{"key":"2_CR10","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"2_CR11","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","volume":"159","author":"K Li","year":"2020","unstructured":"Li, K., Wan, G., Cheng, G., Meng, L., Han, J.: Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS J. Photogramm. Remote. Sens. 159, 296\u2013307 (2020). https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.11.023","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M.M., Yang, J., Li, X.: Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16794\u201316805 (2023)","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Lin, J., Zhao, Y., Wang, S., Tang, Y.: YOLO-DA: an efficient YOLO-based detector for remote sensing object detection. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023). https:\/\/doi.org\/10.1109\/lgrs.2023.3303896 (2023)","DOI":"10.1109\/lgrs.2023.3303896"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Ma, W., et al.: MCDet: multi-content collaboration detector for multiscale remote sensing object. IEEE Geosci. Remote Sens. Lett. (2024)","DOI":"10.1109\/LGRS.2024.3361508"},{"key":"2_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tgrs.2023.3327285","volume":"61","author":"G Peng","year":"2023","unstructured":"Peng, G., Yang, Z., Wang, S., Zhou, Y.: AMFLW-YOLO: a lightweight network for remote sensing image detection based on attention mechanism and multiscale feature fusion. IEEE Trans. Geosci. Remote Sens. 61, 1\u201316 (2023). https:\/\/doi.org\/10.1109\/tgrs.2023.3327285","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2_CR19","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Shi, C., Zheng, X., Zhao, Z., Zhang, K., Su, Z., Lu, Q.: LSKF-YOLO: large selective kernel feature fusion network for power tower detection in high-resolution satellite remote sensing images. IEEE Trans. Geosci. Remote Sens. (2024)","DOI":"10.1109\/TGRS.2024.3389056"},{"key":"2_CR21","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, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2_CR22","unstructured":"Wang, C., et al.: Gold-YOLO: efficient object detector via gather-and-distribute mechanism. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"2_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tgrs.2021.3064599","volume":"60","author":"G Wang","year":"2022","unstructured":"Wang, G., et al.: FSoD-Net: full-scale object detection from optical remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2022). https:\/\/doi.org\/10.1109\/tgrs.2021.3064599","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Xu, S., et al.: HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection. arXiv preprint arXiv:2403.10778 (2024)","DOI":"10.1109\/ICME57554.2024.10687431"},{"key":"2_CR26","first-page":"1","volume":"60","author":"T Xu","year":"2021","unstructured":"Xu, T., Sun, X., Diao, W., Zhao, L., Fu, K., Wang, H.: ASSD: feature aligned single-shot detection for multiscale objects in aerial imagery. IEEE Trans. Geosci. Remote Sens. 60, 1\u201317 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3040273","volume":"60","author":"D Yu","year":"2021","unstructured":"Yu, D., Ji, S.: A new spatial-oriented object detection framework for remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201316 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/lgrs.2022.3212073","volume":"19","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Liu, Y., Huang, Y., Qu, L.: Regional prediction-aware network with cross-scale self-attention for ship detection in SAR images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/lgrs.2022.3212073","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6603-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:43:13Z","timestamp":1777448593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6603-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"ISBN":["9789819666027","9789819666034"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6603-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"9 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}