{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:09:28Z","timestamp":1779365368936,"version":"3.53.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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":"publisher","award":["62371144"],"award-info":[{"award-number":["62371144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00371-026-04484-0","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:05:13Z","timestamp":1778151913000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced small object detection in aerial imagery through context-aware and localization-optimized deep learning"],"prefix":"10.1007","volume":"42","author":[{"given":"Qinghua","family":"Lai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xichun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qizong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangtao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"4484_CR1","doi-asserted-by":"publisher","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","volume":"7","author":"H Shakhatreh","year":"2019","unstructured":"Shakhatreh, H., Sawalmeh, A.H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N.S., Khreishah, A., Guizani, M.: Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7, 48572\u201348634 (2019)","journal-title":"IEEE Access"},{"key":"4484_CR2","doi-asserted-by":"publisher","first-page":"86297","DOI":"10.1109\/ACCESS.2019.2922213","volume":"7","author":"H El-Sayed","year":"2019","unstructured":"El-Sayed, H., Chaqfa, M., Zeadally, S., Puthal, D.: A traffic-aware approach for enabling unmanned aerial vehicles (UAVs) in smart city scenarios. IEEE Access 7, 86297\u201386305 (2019)","journal-title":"IEEE Access"},{"issue":"12","key":"4484_CR3","doi-asserted-by":"publisher","first-page":"69188","DOI":"10.1109\/ACCESS.2024.3401018","volume":"15","author":"F Toscano","year":"2024","unstructured":"Toscano, F., Fiorentino, C., Capece, N., Erra, U., Travascia, D., Scopa, A., Drosos, M., D\u2019Antonio, P.: Unmanned aerial vehicle for precision agriculture: a review. IEEE Access 15(12), 69188\u2013205 (2024)","journal-title":"IEEE Access"},{"key":"4484_CR4","doi-asserted-by":"publisher","first-page":"128967","DOI":"10.1016\/j.neucom.2024.128967","volume":"617","author":"P Li","year":"2025","unstructured":"Li, P., Yuan, X., Haozhi, X., Wang, J., Wang, Y.: EMPViT: efficient multi-path vision transformer for security risks detection in power distribution network. Neurocomputing 617, 128967 (2025)","journal-title":"Neurocomputing"},{"key":"4484_CR5","first-page":"1","volume":"74","author":"M Li","year":"2025","unstructured":"Li, M., Xiong, G., Ye, P., Liu, G., Zhu, F.: Model with leader-follower backbone and bifurcation fusion for UAV traffic object detection. IEEE Trans. Instrum. Meas. 74, 1\u201312 (2025)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4484_CR6","doi-asserted-by":"publisher","first-page":"114602","DOI":"10.1016\/j.eswa.2021.114602","volume":"172","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Sun, P., Wergeles, N., Shang, Y.: A survey and performance evaluation of deep learning methods for small object detection. Expert Syst. Appl. 172, 114602 (2021)","journal-title":"Expert Syst. Appl."},{"key":"4484_CR7","doi-asserted-by":"publisher","first-page":"118665","DOI":"10.1016\/j.eswa.2022.118665","volume":"211","author":"J Xiao","year":"2023","unstructured":"Xiao, J., Guo, H., Zhou, J., Zhao, T., Qiuze, Yu., Chen, Y., Wang, Z.: Tiny object detection with context enhancement and feature purification. Expert Syst. Appl. 211, 118665 (2023)","journal-title":"Expert Syst. Appl."},{"key":"4484_CR8","first-page":"1","volume":"62","author":"X Wang","year":"2024","unstructured":"Wang, X., Chen, H., Chu, X., Wang, P.: Aodet: aerial object detection using transformers for foreground regions. IEEE Trans. Geosci. Remote Sens. 62, 1\u201311 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR9","first-page":"1","volume":"62","author":"H-I Liu","year":"2024","unstructured":"Liu, H.-I., Tseng, Y.-W., Chang, K.-C., Wang, P.-J., Shuai, H.-H., Cheng, W.-H.: A denoising fpn with transformer r-cnn for tiny object detection. IEEE Trans. Geosci. Remote Sens. 62, 1\u201315 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"13","key":"4484_CR10","doi-asserted-by":"publisher","first-page":"24013","DOI":"10.1109\/JIOT.2024.3388045","volume":"11","author":"X Min","year":"2024","unstructured":"Min, X., Zhou, W., Hu, R., Wu, Y., Pang, Y., Yi, J.: Lwuavdet: a lightweight uav object detection network on edge devices. IEEE Internet Things J. 11(13), 24013\u201324023 (2024)","journal-title":"IEEE Internet Things J."},{"key":"4484_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2025.3556469","author":"H Hu","year":"2025","unstructured":"Hu, H., Chen, S.B., Tang, J.: CFENet: contextual feature enhancement network for tiny object detection in aerial images. IEEE Trans. Geosci. Remote Sens. (2025). https:\/\/doi.org\/10.1109\/TGRS.2025.3556469","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR12","doi-asserted-by":"crossref","unstructured":"Liu, X., Yang, M.: The research of small object detection based on yolox in uav. In: 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, pp. 507\u2013512 (2024)","DOI":"10.1109\/CSCWD61410.2024.10580555"},{"issue":"7","key":"4484_CR13","doi-asserted-by":"publisher","first-page":"276","DOI":"10.3390\/drones8070276","volume":"8","author":"M Yue","year":"2024","unstructured":"Yue, M., Zhang, L., Huang, J., Zhang, H.: Lightweight and efficient tiny-object detection based on improved yolov8n for uav aerial images. Drones 8(7), 276 (2024)","journal-title":"Drones"},{"issue":"6","key":"4484_CR14","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","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 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4484_CR15","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":"4484_CR16","unstructured":"Li, C., Li, L., Geng, Y., Jiang, H., Cheng, M., Zhang, B., Ke, Z., Xu, X., Chu, X.: Yolov6 v3.0: a full-scale reloading (2023)"},{"key":"4484_CR17","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.: Yolov9: learning what you want to learn using programmable gradient information (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"4484_CR18","first-page":"107984","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., et al.: 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":"4484_CR19","doi-asserted-by":"publisher","first-page":"124848","DOI":"10.1016\/j.eswa.2024.124848","volume":"256","author":"C Xue","year":"2024","unstructured":"Xue, C., Xia, Y., Mingjie, W., Chen, Z., Cheng, F., Yun, L.: El-yolo: an efficient and lightweight low-altitude aerial objects detector for onboard applications. Expert Syst. Appl. 256, 124848 (2024)","journal-title":"Expert Syst. Appl."},{"key":"4484_CR20","doi-asserted-by":"publisher","first-page":"121366","DOI":"10.1016\/j.ins.2024.121366","volume":"686","author":"Q Fan","year":"2025","unstructured":"Fan, Q., Li, Y., Deveci, M., Zhong, K., Kadry, S.: Lud-yolo: a novel lightweight object detection network for unmanned aerial vehicle. Inf. Sci. 686, 121366 (2025)","journal-title":"Inf. Sci."},{"key":"4484_CR21","doi-asserted-by":"publisher","first-page":"113936","DOI":"10.1016\/j.measurement.2023.113936","volume":"224","author":"Y Hui","year":"2024","unstructured":"Hui, Y., Wang, J., Li, B.: STF-YOLO: a small target detection algorithm for UAV remote sensing images based on improved swin transformer and class weighted classification decoupling head. Measurement 224, 113936 (2024)","journal-title":"Measurement"},{"key":"4484_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2025.3551917","author":"S Tao","year":"2025","unstructured":"Tao, S., Shengqi, Y., Haiying, L., Jason, G., Lixia, D., Lida, L.: Mis-yolov8: an improved algorithm for detecting small objects in uav aerial photography based on yolov8. IEEE Trans. Instrum. Meas. (2025). https:\/\/doi.org\/10.1109\/TIM.2025.3551917","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4484_CR23","doi-asserted-by":"publisher","first-page":"126384","DOI":"10.1016\/j.neucom.2023.126384","volume":"547","author":"T Wang","year":"2023","unstructured":"Wang, T., Ma, Z., Yang, T., Zou, S.: Petnet: a yolo-based prior enhanced transformer network for aerial image detection. Neurocomputing 547, 126384 (2023)","journal-title":"Neurocomputing"},{"key":"4484_CR24","doi-asserted-by":"publisher","first-page":"5082","DOI":"10.1109\/JSTARS.2025.3526995","volume":"18","author":"X Zhang","year":"2025","unstructured":"Zhang, X., Feng, Y., Wang, N., Guohua, L., Mei, S.: Transformer-based person detection in paired RGB-T aerial images with VTSaR dataset. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 18, 5082\u20135099 (2025)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"issue":"11","key":"4484_CR25","first-page":"13467","volume":"45","author":"G Cheng","year":"2023","unstructured":"Cheng, G., Yuan, X., Yao, X., Yan, K., Zeng, Q., Xie, X., Han, J.: Towards large-scale small object detection: survey and benchmarks. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 13467\u201313488 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4484_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2025.3556469","author":"H Hu","year":"2025","unstructured":"Hu, H., Chen, S.-B., Tang, J.: Cfenet: contextual feature enhancement network for tiny object detection in aerial images. IEEE Trans. Geosci. Remote Sens. (2025). https:\/\/doi.org\/10.1109\/TGRS.2025.3556469","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2026.3654892","author":"X Yuan","year":"2026","unstructured":"Yuan, X., Cheng, G., Cheng, J., Yao, R., Han, J.: Unc-sod: an uncertainty learning framework for small object detection. IEEE Trans. Image Process. (2026). https:\/\/doi.org\/10.1109\/TIP.2026.3654892","journal-title":"IEEE Trans. Image Process."},{"key":"4484_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2025.3545522","author":"R Wang","year":"2025","unstructured":"Wang, R., Lin, C., Li, Y.: Rplfdet: a lightweight small object detection network for uav aerial images with rational preservation of low-level features. IEEE Trans. Instrum. Meas. (2025). https:\/\/doi.org\/10.1109\/TIM.2025.3545522","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"8","key":"4484_CR29","doi-asserted-by":"publisher","first-page":"5733","DOI":"10.1007\/s00371-024-03749-w","volume":"41","author":"X Zhang","year":"2025","unstructured":"Zhang, X., Yang, D., Song, T., Ye, Y., Song, Y., Zhou, J., Chen, J.: A lightweight object detector based on changeable-size lightweight convolution and context augmentation module for images captured by UAVs. Vis. Comput. 41(8), 5733\u20135749 (2025)","journal-title":"Vis. Comput."},{"issue":"6","key":"4484_CR30","doi-asserted-by":"publisher","first-page":"5966","DOI":"10.1109\/TCSVT.2025.3532243","volume":"35","author":"X Yuan","year":"2025","unstructured":"Yuan, X., Cheng, G., Yao, R., Han, J.: Semantic differentiation aids oriented small object detection. IEEE Trans. Circuits Syst. Video Technol. 35(6), 5966\u20135979 (2025)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4484_CR31","first-page":"1","volume":"62","author":"Y Xiao","year":"2024","unstructured":"Xiao, Y., Xu, T., Yu, X., Fang, Y., Li, J.: A lightweight fusion strategy with enhanced interlayer feature correlation for small object detection. IEEE Trans. Geosci. Remote Sens. 62, 1\u201311 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR32","first-page":"1","volume":"62","author":"M Yin Zhang","year":"2024","unstructured":"Yin Zhang, M., Ye, G.Z., Liu, Y., Guo, P., Yan, J.: Ffca-yolo for small object detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 62, 1\u201315 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR33","first-page":"1","volume":"73","author":"L Jiang","year":"2024","unstructured":"Jiang, L., Yuan, B., Du, J., Chen, B., Xie, H., Tian, J., Yuan, Z.: MFFSODNet: multiscale feature fusion small object detection network for UAV aerial images. IEEE Trans. Instrum. Meas. 73, 1\u201314 (2024)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4484_CR34","first-page":"1","volume":"63","author":"J Song","year":"2024","unstructured":"Song, J., Zhou, M., Luo, J., Pu, H., Feng, Y., Wei, X., Jia, W.: Boundary-aware feature fusion with dual-stream attention for remote sensing small object detection. IEEE Trans. Geosci. Remote Sens. 63, 1\u201313 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR35","doi-asserted-by":"publisher","first-page":"119997","DOI":"10.1016\/j.eswa.2023.119997","volume":"224","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Xia, K., Huang, Z., Wang, S., Akindele, R.G.: Etam: ensemble transformer with attention modules for detection of small objects. Expert Syst. Appl. 224, 119997 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"4484_CR36","doi-asserted-by":"publisher","first-page":"4603","DOI":"10.1007\/s00371-024-03680-0","volume":"41","author":"T Huo","year":"2025","unstructured":"Huo, T., Liu, Z., Zhang, S., Jiening, W., Yuan, R., Duan, S., Wang, L.: CDNet: object detection based on cross-level aggregation and deformable attention for UAV aerial images. Vis. Comput. 41(7), 4603\u20134621 (2025)","journal-title":"Vis. Comput."},{"key":"4484_CR37","doi-asserted-by":"publisher","first-page":"4371","DOI":"10.1109\/JSTARS.2022.3175498","volume":"15","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Feng, Y., Zhang, S., Wang, N., Mei, S.: Finding nonrigid tiny person with densely cropped and local attention object detector networks in low-altitude aerial images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15, 4371\u20134385 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"4484_CR38","unstructured":"Iandola, F.N.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360, (2016)"},{"key":"4484_CR39","unstructured":"Howard, A.\u00a0G.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, (2017)"},{"key":"4484_CR40","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4484_CR41","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V. et\u00a0al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"4484_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"4484_CR43","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. PMLR, pp. 6105\u20136114 (2019)"},{"key":"4484_CR44","unstructured":"Bochkovskiy, A., Wang, C.-Y., Mark Liao, H.-Y.: Yolov4: optimal speed and accuracy of object detection (2020)"},{"key":"4484_CR45","unstructured":"Jocher.: Ultralytics yolov5 (2020)"},{"key":"4484_CR46","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Mark Liao H.-Y.: 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":"4484_CR47","doi-asserted-by":"publisher","first-page":"121036","DOI":"10.1016\/j.eswa.2023.121036","volume":"234","author":"B Mahaur","year":"2023","unstructured":"Mahaur, B., Mishra, K.K., Kumar, A.: An improved lightweight small object detection framework applied to real-time autonomous driving. Expert Syst. Appl. 234, 121036 (2023)","journal-title":"Expert Syst. Appl."},{"key":"4484_CR48","doi-asserted-by":"publisher","first-page":"127210","DOI":"10.1016\/j.neucom.2023.127210","volume":"574","author":"X Liu","year":"2024","unstructured":"Liu, X., Wang, T., Yang, J., Tang, C., Lv, J.: Mpq-yolo: ultra low mixed-precision quantization of yolo for edge devices deployment. Neurocomputing 574, 127210 (2024)","journal-title":"Neurocomputing"},{"issue":"1","key":"4484_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40747-024-01676-w","volume":"11","author":"S Jobaer","year":"2025","unstructured":"Jobaer, S., Tang, X., Zhang, Y., Li, G., Ahmed, F.: A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images. Complex Intell. Syst. 11(1), 1\u201327 (2025)","journal-title":"Complex Intell. Syst."},{"key":"4484_CR50","first-page":"1","volume":"62","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Feng, Y., Zhang, S., Wang, N., Guohua, L., Mei, S.: Robust aerial person detection with lightweight distillation network for edge deployment. IEEE Trans. Geosci. Remote Sens. 62, 1\u201316 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4484_CR51","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern recognition, pp. 9308\u20139316 (2019)","DOI":"10.1109\/CVPR.2019.00953"},{"key":"4484_CR52","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, Di. et\u00a0al.: Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 385\u2013400 (2018)","DOI":"10.1007\/978-3-030-01252-6_24"},{"issue":"4","key":"4484_CR53","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4484_CR54","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"4484_CR55","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":"4484_CR56","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":"4484_CR57","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.\u00a0V.: 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":"4484_CR58","unstructured":"Du, D., Zhu, P., Wen, L., Bian, X., Lin, H., Hu, Q., Peng, T., Zheng, J., Wang, X., Zhang, Y. et\u00a0al.: Visdrone-det2019: the vision meets drone object detection in image challenge results. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 0\u20130 (2019)"},{"key":"4484_CR59","unstructured":"Wang, J., Xu, C., Yang, W., Yu, L.: A normalized gaussian wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389, (2021)"},{"key":"4484_CR60","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, W., Guo, H., Zhang, R., Xia, G.-S.: Tiny object detection in aerial images. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp. 3791\u20133798 (2021)","DOI":"10.1109\/ICPR48806.2021.9413340"},{"key":"4484_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.\u00a0Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"4484_CR62","first-page":"21002","volume":"33","author":"X Li","year":"2020","unstructured":"Li, X., Wang, W., Lijun, W., Chen, S., Xiaolin, H., Li, J., Tang, J., Yang, J.: Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. Adv. Neural. Inf. Process. Syst. 33, 21002\u201321012 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4484_CR63","unstructured":"Ross, T.-Y., Doll\u00e1r, G.: Focal loss for dense object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2980\u20132988 (2017)"},{"key":"4484_CR64","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, (2021)"},{"key":"4484_CR65","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"},{"key":"4484_CR66","doi-asserted-by":"crossref","unstructured":"Jixiang, W., Pan, Z., Lei, B., Yuxin, H.: Fsanet: feature-and-spatial-aligned network for tiny object detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201317 (2022)","DOI":"10.1109\/TGRS.2022.3205052"},{"key":"4484_CR67","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.\u00a0R., Huang, W.: Tood: task-aligned one-stage object detection. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE, pp. 3490\u20133499. Computer Society, (2021)","DOI":"10.1109\/ICCV48922.2021.00349"},{"issue":"37","key":"4484_CR68","doi-asserted-by":"publisher","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","volume":"4","author":"D Gunning","year":"2019","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.-Z.: Xai-explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019)","journal-title":"Sci. Robot."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04484-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:43:25Z","timestamp":1779363805000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04484-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":68,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["4484"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04484-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"31 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2026","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"285"}}