{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:48:48Z","timestamp":1774554528740,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"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":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11554-024-01550-5","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T09:02:24Z","timestamp":1725613344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["LGFF-YOLO: small object detection method of UAV images based on efficient local\u2013global feature fusion"],"prefix":"10.1007","volume":"21","author":[{"given":"Hongxing","family":"Peng","sequence":"first","affiliation":[]},{"given":"Haopei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Huanai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xianlu","family":"Guan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,6]]},"reference":[{"key":"1550_CR1","doi-asserted-by":"publisher","first-page":"19683","DOI":"10.1007\/s11042-021-11146-x","volume":"81","author":"H Gupta","year":"2022","unstructured":"Gupta, H., Verma, O.P.: Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach. Multimed. Tools Appl. 81, 19683\u201319703 (2022)","journal-title":"Multimed. Tools Appl."},{"issue":"12","key":"1550_CR2","doi-asserted-by":"publisher","first-page":"13582","DOI":"10.3934\/mbe.2022634","volume":"19","author":"X Liu","year":"2022","unstructured":"Liu, X., Xing, Z., Liu, H., Peng, H., Xu, H., Yuan, J., Gou, Z.: Combination of UAV and raspberry Pi 4B: airspace detection of red imported fire ant nests using an improved YOLOv4 model. Math. Biosci. Eng. (MBE) 19(12), 13582\u201313606 (2022)","journal-title":"Math. Biosci. Eng. (MBE)"},{"issue":"12","key":"1550_CR3","doi-asserted-by":"publisher","first-page":"3300","DOI":"10.1049\/ipr2.12565","volume":"16","author":"Y Xue","year":"2022","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Yang, J., Hou, X.: Mobiletrack: siamese efficient mobile network for high-speed UAV tracking. IET Image Proc. 16(12), 3300\u20133313 (2022)","journal-title":"IET Image Proc."},{"issue":"9","key":"1550_CR4","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.cja.2023.03.048","volume":"36","author":"X Yuanliang","year":"2023","unstructured":"Yuanliang, X., Guodong, J., Tao, S., Lining, T., Lianfeng, W.: Template-guided frequency attention and adaptive cross-entropy loss for UAV visual tracking. Chin. J. Aeronaut. 36(9), 299\u2013312 (2023)","journal-title":"Chin. J. Aeronaut."},{"key":"1550_CR5","doi-asserted-by":"publisher","first-page":"3305728","DOI":"10.1109\/TGRS.2023.3305728","volume":"61","author":"Y Xue","year":"2023","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Wang, N., Gao, J., Wang, L.: Smalltrack: wavelet pooling and graph enhanced classification for UAV small object tracking. IEEE Trans. Geosci. Remote Sens. 61, 3305728 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"1550_CR6","doi-asserted-by":"publisher","unstructured":"Xue, Y., Jin, G., Shen, T., Tan, L., Wang, N., Gao, J., Wang, L.: Consistent representation mining for multi-drone single object tracking. IEEE Trans. Circuits Syst. Video Technol. (2024). https:\/\/doi.org\/10.1109\/TCSVT.2024.3411301","DOI":"10.1109\/TCSVT.2024.3411301"},{"key":"1550_CR7","doi-asserted-by":"publisher","unstructured":"Xue, Y., Shen, T., Jin, G., Tan, L., Wang, N., Wang, L., Gao, J.: Handling occlusion in uav visual tracking with query-guided redetection. IEEE Trans. Instrum. Meas. (2024). https:\/\/doi.org\/10.1109\/TIM.2024.3440378","DOI":"10.1109\/TIM.2024.3440378"},{"key":"1550_CR8","unstructured":"Zhang, X., Liu, C., Yang, D., Song, T., Ye, Y., Li, K., Song, Y.: Rfaconv: innovating spatial attention and standard convolutional operation (2023). arXiv preprint arXiv:2304.03198"},{"key":"1550_CR9","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., Zhang, L.: Dynamic head: unifying object detection heads with attentions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7373\u20137382 (2021)","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"1550_CR10","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\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021\u201312031 (2023)","DOI":"10.1109\/CVPR52729.2023.01157"},{"issue":"7","key":"1550_CR11","doi-asserted-by":"publisher","first-page":"7717","DOI":"10.1109\/TITS.2022.3193909","volume":"24","author":"L Yang","year":"2022","unstructured":"Yang, L., Zhong, J., Zhang, Y., Bai, S., Li, G., Yang, Y., Zhang, J.: An improving faster-rcnn with multi-attention resnet for small target detection in intelligent autonomous transport with 6g. IEEE Trans. Intell. Transp. Syst. 24(7), 7717\u20137725 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"1550_CR12","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1109\/TII.2021.3061419","volume":"18","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Xu, X., Liang, W., Zeng, Z., Shimizu, S., Yang, L.T., Jin, Q.: Intelligent small object detection for digital twin in smart manufacturing with industrial cyber-physical systems. IEEE Trans. Ind. Inform. 18(2), 1377\u20131386 (2021)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"1550_CR13","first-page":"211","volume":"15","author":"H Peng","year":"2022","unstructured":"Peng, H., Xue, C., Shao, Y., Chen, K., Liu, H., Xiong, J., Chen, H., Gao, Z., Yang, Z.: Litchi detection in the field using an improved YOLOv3 model. Int. J. Agric. Biol. Eng. 15(2), 211\u2013220 (2022)","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"1550_CR14","doi-asserted-by":"crossref","unstructured":"Chen, C., Liu, M.-Y., Tuzel, O., Xiao, J.: R-cnn for small object detection. In: Computer Vision\u2014ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20\u201324, 2016, Revised Selected Papers, Part V 13, pp. 214\u2013230. Springer (2017)","DOI":"10.1007\/978-3-319-54193-8_14"},{"key":"1550_CR15","doi-asserted-by":"crossref","unstructured":"Krishna, H., Jawahar, C.: Improving small object detection. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 340\u2013345. IEEE (2017)","DOI":"10.1109\/ACPR.2017.149"},{"key":"1550_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, S., Thachan, S., Chen, J., Qian, Y.: Deconv r-cnn for small object detection on remote sensing images. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 2483\u20132486. IEEE (2018)","DOI":"10.1109\/IGARSS.2018.8517436"},{"key":"1550_CR17","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1550_CR18","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"1550_CR19","doi-asserted-by":"crossref","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: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337. Springer (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1550_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122669","volume":"241","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Zhang, H., Huang, Q., Han, Y., Zhao, M.: Dsp-yolo: an anchor-free network with dspan for small object detection of multiscale defects. Expert Syst. Appl. 241, 122669 (2024)","journal-title":"Expert Syst. Appl."},{"key":"1550_CR21","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":"1550_CR22","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: Panet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9197\u20139206 (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"1550_CR23","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":"1550_CR24","doi-asserted-by":"crossref","unstructured":"Yang, G., Lei, J., Zhu, Z., Cheng, S., Feng, Z., Liang, R.: Afpn: asymptotic feature pyramid network for object detection. In: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2184\u20132189. IEEE (2023)","DOI":"10.1109\/SMC53992.2023.10394415"},{"key":"1550_CR25","doi-asserted-by":"crossref","unstructured":"Jin, Z., Yu, D., Song, L., Yuan, Z., Yu, L.: You should look at all objects. In: European Conference on Computer Vision, pp. 332\u2013349. Springer (2022)","DOI":"10.1007\/978-3-031-20077-9_20"},{"key":"1550_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tan, S., Tong, Y.: Semantic flow for fast and accurate scene parsing. In: Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16, pp. 775\u2013793. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_45"},{"key":"1550_CR27","doi-asserted-by":"publisher","first-page":"9445","DOI":"10.1109\/TIP.2020.3028196","volume":"29","author":"Z Jin","year":"2020","unstructured":"Jin, Z., Liu, B., Chu, Q., Yu, N.: Safnet: a semi-anchor-free network with enhanced feature pyramid for object detection. IEEE Trans. Image Process. 29, 9445\u20139457 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"1550_CR28","doi-asserted-by":"publisher","first-page":"9099","DOI":"10.1109\/TIP.2021.3118953","volume":"30","author":"P-Y Chen","year":"2021","unstructured":"Chen, P.-Y., Chang, M.-C., Hsieh, J.-W., Chen, Y.-S.: Parallel residual bi-fusion feature pyramid network for accurate single-shot object detection. IEEE Trans. Image Process. 30, 9099\u20139111 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1550_CR29","unstructured":"Wang, C., He, W., Nie, Y., Guo, J., Liu, C., Wang, Y., Han, K.: Gold-yolo: efficient object detector via gather-and-distribute mechanism. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"1550_CR30","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: making vgg-style convnets great again. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733\u201313742 (2021)","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1550_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"1550_CR32","doi-asserted-by":"publisher","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","volume":"44","author":"P Zhu","year":"2021","unstructured":"Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., Ling, H.: Detection and tracking meet drones challenge. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7380\u20137399 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1550_CR33","doi-asserted-by":"crossref","unstructured":"Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L.: Dota: A large-scale dataset for object detection in aerial images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00418"},{"key":"1550_CR34","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1550_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: Varifocalnet: an iou-aware dense object detector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8514\u20138523 (2021)","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"1550_CR36","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1550_CR37","doi-asserted-by":"publisher","unstructured":"Jocher, G.: Ultralytics YOLOv5. https:\/\/doi.org\/10.5281\/zenodo.3908559","DOI":"10.5281\/zenodo.3908559"},{"key":"1550_CR38","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 (2022). arXiv preprint arXiv:2207.02696","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1550_CR39","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLOv8. https:\/\/github.com\/ultralytics\/ultralytics"},{"issue":"15","key":"1550_CR40","doi-asserted-by":"publisher","first-page":"6811","DOI":"10.3390\/s23156811","volume":"23","author":"Z Su","year":"2023","unstructured":"Su, Z., Yu, J., Tan, H., Wan, X., Qi, K.: Msa-yolo: a remote sensing object detection model based on multi-scale strip attention. Sensors 23(15), 6811 (2023)","journal-title":"Sensors"},{"issue":"22","key":"1550_CR41","doi-asserted-by":"publisher","first-page":"12369","DOI":"10.3390\/app132212369","volume":"13","author":"Z Huangfu","year":"2023","unstructured":"Huangfu, Z., Li, S.: Lightweight you only look once v8: an upgraded you only look once v8 algorithm for small object identification in unmanned aerial vehicle images. Appl. Sci. 13(22), 12369 (2023)","journal-title":"Appl. Sci."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01550-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01550-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01550-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T15:22:16Z","timestamp":1729005736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01550-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,6]]},"references-count":41,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1550"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01550-5","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,6]]},"assertion":[{"value":"10 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"167"}}