{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T04:38:42Z","timestamp":1783744722616,"version":"3.55.0"},"reference-count":87,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing: Image Communication"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.image.2026.117578","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T23:30:24Z","timestamp":1778196624000},"page":"117578","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["DARE-YOLO: A small object detection network for UAV scenarios via feature enhancement and dynamic multi-scale fusion"],"prefix":"10.1016","volume":"146","author":[{"given":"Xinyu","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhui","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.image.2026.117578_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Radoglou-Grammatikis","year":"2020","journal-title":"Comput. Netw."},{"issue":"3","key":"10.1016\/j.image.2026.117578_b2","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1177\/15485129211031668","article-title":"The role of unmanned aerial vehicles in military communications: application scenarios, current trends, and beyond","volume":"21","author":"Gargalakos","year":"2024","journal-title":"J. Def. Model. Simul."},{"issue":"6","key":"10.1016\/j.image.2026.117578_b3","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1002\/rob.22075","article-title":"Emerging UAV technology for disaster detection, mitigation, response, and preparedness","volume":"39","author":"Khan","year":"2022","journal-title":"J. Field Robot."},{"issue":"1","key":"10.1016\/j.image.2026.117578_b4","doi-asserted-by":"crossref","first-page":"217","DOI":"10.3390\/en15010217","article-title":"Unmanned aerial vehicles (UAV) in precision agriculture: Applications and challenges","volume":"15","author":"Velusamy","year":"2021","journal-title":"Energies"},{"key":"10.1016\/j.image.2026.117578_b5","article-title":"FMTrack: frequency-aware interaction and multi-expert fusion for RGB-T tracking","author":"Xue","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2026.117578_b6","doi-asserted-by":"crossref","DOI":"10.1109\/TCSVT.2025.3549953","article-title":"Avltrack: Dynamic sparse learning for aerial vision-language tracking","author":"Xue","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.image.2026.117578_b7","article-title":"Handling occlusion in uav visual tracking with query-guided redetection","author":"Xue","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.image.2026.117578_b8","article-title":"Target-distractor aware UAV tracking via global agent","author":"Xue","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"10.1016\/j.image.2026.117578_b9","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117578_b10","doi-asserted-by":"crossref","unstructured":"K. He, G. Gkioxari, P. Doll\u00e1r, R. Girshick, Mask r-cnn, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.1016\/j.image.2026.117578_b11","doi-asserted-by":"crossref","unstructured":"Z. Cai, N. Vasconcelos, Cascade R-CNN: Delving Into High Quality Object Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 6154\u20136162.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"10.1016\/j.image.2026.117578_b12","doi-asserted-by":"crossref","unstructured":"Y. Gong, X. Yu, Y. Ding, X. Peng, J. Zhao, Z. Han, Effective fusion factor in FPN for tiny object detection, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2021, pp. 1160\u20131168.","DOI":"10.1109\/WACV48630.2021.00120"},{"issue":"3","key":"10.1016\/j.image.2026.117578_b13","doi-asserted-by":"crossref","first-page":"190","DOI":"10.3390\/drones7030190","article-title":"Yolo-based uav technology: A review of the research and its applications","volume":"7","author":"Chen","year":"2023","journal-title":"Drones"},{"key":"10.1016\/j.image.2026.117578_b14","doi-asserted-by":"crossref","unstructured":"J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.1016\/j.image.2026.117578_b15","series-title":"Yolov4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"10.1016\/j.image.2026.117578_b16","doi-asserted-by":"crossref","unstructured":"C.Y. Wang, A. Bochkovskiy, H.Y.M. Liao, 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, 2023, pp. 7464\u20137475.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"10.1016\/j.image.2026.117578_b17","series-title":"Yolov11: An overview of the key architectural enhancements","author":"Khanam","year":"2024"},{"key":"10.1016\/j.image.2026.117578_b18","doi-asserted-by":"crossref","unstructured":"T.Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.image.2026.117578_b19","doi-asserted-by":"crossref","unstructured":"K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, Q. Tian, Centernet: Keypoint triplets for object detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 6569\u20136578.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"10.1016\/j.image.2026.117578_b20","series-title":"European Conference on Computer Vision","first-page":"213","article-title":"End-to-end object detection with transformers","author":"Carion","year":"2020"},{"key":"10.1016\/j.image.2026.117578_b21","series-title":"Deformable detr: Deformable transformers for end-to-end object detection","author":"Zhu","year":"2020"},{"key":"10.1016\/j.image.2026.117578_b22","first-page":"4643","article-title":"Remdet: Rethinking efficient model design for uav object detection","volume":"vol. 39","author":"Li","year":"2025"},{"key":"10.1016\/j.image.2026.117578_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2024.103944","article-title":"GMC: A general framework of multi-stage context learning and utilization for visual detection tasks","volume":"241","author":"Wang","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.image.2026.117578_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2024.100342","article-title":"Enhancing object detection in low-resolution images via frequency domain learning","volume":"22","author":"Gao","year":"2024","journal-title":"Array"},{"issue":"1","key":"10.1016\/j.image.2026.117578_b25","doi-asserted-by":"crossref","first-page":"24183","DOI":"10.1038\/s41598-025-10286-6","article-title":"Low resolution remote sensing object detection with fine grained enhancement and swin transformer","volume":"15","author":"Xu","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.image.2026.117578_b26","doi-asserted-by":"crossref","unstructured":"S.A. Hussein, T. Tirer, R. Giryes, Correction filter for single image super-resolution: Robustizing off-the-shelf deep super-resolvers, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2020, pp. 1428\u20131437.","DOI":"10.1109\/CVPR42600.2020.00150"},{"key":"10.1016\/j.image.2026.117578_b27","first-page":"51094","article-title":"Gold-YOLO: Efficient object detector via gather-and-distribute mechanism","volume":"vol. 36","author":"Wang","year":"2023"},{"key":"10.1016\/j.image.2026.117578_b28","article-title":"Cross-layer feature pyramid transformer for small object detection in aerial images","author":"Du","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.image.2026.117578_b29","article-title":"ESOD: efficient small object detection on high-resolution images","author":"Liu","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117578_b30","unstructured":"J. Li, Y. Wang, H. Zhang, X. Liu, F. Yang, Drone-YOLO: Efficient Object Detection for Drone-Captured Images, in: Proceedings of the IEEE International Conference on Unmanned Aircraft Systems, ICUAS, 2022, pp. 1084\u20131091."},{"issue":"4","key":"10.1016\/j.image.2026.117578_b31","first-page":"1","article-title":"YOLO-SDLUWD: Spatial-detail enhancement and background suppression for small object detection in UAV imagery","volume":"63","author":"Li","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"10.1016\/j.image.2026.117578_b32","first-page":"17890","article-title":"BRSTD: Bidirectional region-aware feature fusion network for small target detection under complex backgrounds","volume":"24","author":"Chen","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.image.2026.117578_b33","article-title":"DMCTDet: Dense multi-scale context transfer for small object detection in UAV scenes","volume":"156","author":"Zhang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.image.2026.117578_b34","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"2117","article-title":"Feature pyramid networks for object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.image.2026.117578_b35","doi-asserted-by":"crossref","unstructured":"W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S.E. Reed, C.Y. Fu, A.C. Berg, SSD: Single Shot MultiBox Detector, in: Proceedings of the European Conference on Computer Vision, ECCV, 2016, pp. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10.1016\/j.image.2026.117578_b36","doi-asserted-by":"crossref","unstructured":"T.Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal Loss for Dense Object Detection, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.image.2026.117578_b37","doi-asserted-by":"crossref","unstructured":"S. Zhang, C. Chi, Y. Yao, Z. Lei, S.Z. Li, 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, pp. 9387\u20139396.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"10.1016\/j.image.2026.117578_b38","doi-asserted-by":"crossref","unstructured":"C. Yang, Z. Huang, N. Wang, QueryDet: Cascaded sparse query for accelerating high-resolution small object detection, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2022, pp. 13668\u201313677.","DOI":"10.1109\/CVPR52688.2022.01330"},{"key":"10.1016\/j.image.2026.117578_b39","doi-asserted-by":"crossref","unstructured":"Y. Chen, X. Dai, M. Liu, et al., Dynamic convolution: Attention over convolution kernels, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2020, pp. 11030\u201311039.","DOI":"10.1109\/CVPR42600.2020.01104"},{"issue":"8","key":"10.1016\/j.image.2026.117578_b40","first-page":"4215","article-title":"DMTNet: Dynamic multi-scale transformer network for small object detection in UAV imagery","volume":"33","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"9","key":"10.1016\/j.image.2026.117578_b41","article-title":"TFDNet: A triple focus diffusion network for object detection in urban congestion with accurate multi-scale feature fusion and real-time capability","volume":"36","author":"Gu","year":"2024","journal-title":"J. King Saud University-Computer Inf. Sci."},{"key":"10.1016\/j.image.2026.117578_b42","article-title":"Context-aware semantic fusion with background suppression for UAV small object detection","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.image.2026.117578_b43","article-title":"Spatial-semantic interaction module for enhancing small target-background discrimination in UAV imagery","volume":"129","author":"Wang","year":"2024","journal-title":"Signal Process., Image Commun."},{"key":"10.1016\/j.image.2026.117578_b44","first-page":"345","article-title":"YOLO-DC: Dynamic channel fusion for semantic enhancement in UAV small object detection","volume":"598","author":"Li","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.image.2026.117578_b45","series-title":"Deformable object tracking with gated fusion","author":"Liu","year":"2018"},{"key":"10.1016\/j.image.2026.117578_b46","first-page":"1","article-title":"Multi-oriented object detection in aerial images with double horizontal rectangles","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.image.2026.117578_b47","doi-asserted-by":"crossref","unstructured":"J. Dai, H. Qi, Y. Xiong, et al., Deformable convolutional networks, in: Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 764\u2013773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"10.1016\/j.image.2026.117578_b48","doi-asserted-by":"crossref","unstructured":"W. Liu, H. Lu, H. Fu, et al., Learning to upsample by learning to sample, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis., 2023, pp. 6027\u20136037.","DOI":"10.1109\/ICCV51070.2023.00554"},{"key":"10.1016\/j.image.2026.117578_b49","series-title":"PixelShuffler: A simple image translation through pixel rearrangement","author":"Zamzam","year":"2024"},{"issue":"2","key":"10.1016\/j.image.2026.117578_b50","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/0304-3991(81)90061-9","article-title":"Bilinear interpolation of digital images","volume":"6","author":"Smith","year":"1981","journal-title":"Ultramicroscopy"},{"issue":"9","key":"10.1016\/j.image.2026.117578_b51","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1080\/2150704X.2024.2388853","article-title":"MAFPN: a mixed local-global attention feature pyramid network for aerial object detection","volume":"15","author":"Ma","year":"2024","journal-title":"Remote. Sens. Lett."},{"key":"10.1016\/j.image.2026.117578_b52","doi-asserted-by":"crossref","unstructured":"X. Pan, C. Ge, R. Lu, et al., On the integration of self-attention and convolution, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2022, pp. 815\u2013825.","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"10.1016\/j.image.2026.117578_b53","first-page":"24261","article-title":"MLP-mixer: An all-MLP architecture for vision","volume":"34","author":"Tolstikhin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117578_b54","unstructured":"D. Du, P. Zhu, L. Wen, et al., VisDrone-DET2019: The vision meets drone object detection in image challenge results, in: Proc. IEEE\/CVF Int. Conf. Comput. Vis. Workshops, 2019."},{"key":"10.1016\/j.image.2026.117578_b55","article-title":"The unmanned aerial vehicle benchmark: Object detection and tracking","author":"Du","year":"2018","journal-title":"Eur. Conf. Comput. Vis."},{"key":"10.1016\/j.image.2026.117578_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2022.104499","article-title":"SODA: A large-scale open site object detection dataset for deep learning in construction","volume":"142","author":"Duan","year":"2022","journal-title":"Autom. Constr."},{"key":"10.1016\/j.image.2026.117578_b57","article-title":"Ultralytics\/yolov5: v3.0","author":"Jocher","year":"2020","journal-title":"Zenodo"},{"key":"10.1016\/j.image.2026.117578_b58","series-title":"Int. Conf. Data Intell. Cogn. Inform.","first-page":"529","article-title":"A review on YOLOv8 and its advancements","author":"Sohan","year":"2024"},{"key":"10.1016\/j.image.2026.117578_b59","doi-asserted-by":"crossref","unstructured":"C.Y. Wang, A. Bochkovskiy, H.Y.M. Liao, YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2023, pp. 10954\u201310964.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"10.1016\/j.image.2026.117578_b60","first-page":"107984","article-title":"YOLOv10: Real-time end-to-end object detection","volume":"vol. 37","author":"Wang","year":"2024"},{"key":"10.1016\/j.image.2026.117578_b61","series-title":"YOLOv12: Next-generation real-time object detection","author":"Jocher","year":"2024"},{"key":"10.1016\/j.image.2026.117578_b62","series-title":"RT-DETRv3: Real-time end-to-end object detection with hierarchical dense positive supervision","author":"Wang","year":"2024"},{"key":"10.1016\/j.image.2026.117578_b63","doi-asserted-by":"crossref","unstructured":"Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, et al., Detrs beat yolos on real-time object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 16965\u201316974.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"10.1016\/j.image.2026.117578_b64","article-title":"Adaptive deformation-learning and multiscale-integrated network for remote sensing object detection","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.image.2026.117578_b65","series-title":"Gcrh: A global context reconstruction hybrid detector for enhanced small object detection in UAV aerial imagery","author":"Xue","year":"2025"},{"issue":"8","key":"10.1016\/j.image.2026.117578_b66","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s40747-025-01971-0","article-title":"LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images","volume":"11","author":"Ma","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.image.2026.117578_b67","article-title":"KSCNet: Exploring KAN and state space model collaboration network for small object detection from UAV imagery","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.image.2026.117578_b68","article-title":"RSW-YOLO: Rotated spatial weighting for aerial object detection","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.image.2026.117578_b69","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, et al., Grad-CAM: Visual explanations from deep networks via gradient-based localization, in: Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"10.1016\/j.image.2026.117578_b70","series-title":"YOLOv6: A single-stage object detection framework for industrial applications","author":"Li","year":"2022"},{"key":"10.1016\/j.image.2026.117578_b71","doi-asserted-by":"crossref","unstructured":"A. Meethal, E. Granger, M. Pedersoli, Cascaded zoom-in detector for high resolution aerial images, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2046\u20132055.","DOI":"10.1109\/CVPRW59228.2023.00198"},{"key":"10.1016\/j.image.2026.117578_b72","doi-asserted-by":"crossref","unstructured":"B. Du, Y. Huang, J. Chen, D. Huang, Adaptive sparse convolutional networks with global context enhancement for faster object detection on drone images, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 13435\u201313444.","DOI":"10.1109\/CVPR52729.2023.01291"},{"key":"10.1016\/j.image.2026.117578_b73","first-page":"1234","article-title":"ClusDet: Cluster proposal network for dense UAV object detection","volume":"30","author":"Yang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117578_b74","article-title":"DMNet: Density map network for small object detection in aerial images","author":"Li","year":"2020","journal-title":"CVPR Work."},{"key":"10.1016\/j.image.2026.117578_b75","article-title":"BAP-DETR: Efficient drone object detection network based on bipartite attentive processing and dual fusion encoder","volume":"104565","author":"Wang","year":"2025","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.image.2026.117578_b76","series-title":"DETRs beat YOLOs on real-time object detection","author":"Zhao","year":"2023"},{"issue":"4","key":"10.1016\/j.image.2026.117578_b77","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding vertex on the horizontal bounding box for multi-oriented object detection","volume":"43","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117578_b78","doi-asserted-by":"crossref","unstructured":"S. Yang, Z. Pei, F. Zhou, Rotated Faster R-CNN for oriented object detection in aerial images, in: Proc. 3rd Int. Conf. Robot Syst. Appl., 2020, pp. 35\u201339.","DOI":"10.1145\/3402597.3402605"},{"key":"10.1016\/j.image.2026.117578_b79","doi-asserted-by":"crossref","unstructured":"X. Yuan, G. Cheng, K. Yan, Q. Zeng, J. Han, Small object detection via coarse-to-fine proposal generation and imitation learning, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 6317\u20136327.","DOI":"10.1109\/ICCV51070.2023.00581"},{"key":"10.1016\/j.image.2026.117578_b80","first-page":"1","article-title":"M2VDet: Midpoints-to-vertices detection of oriented objects in remote-sensing images","volume":"61","author":"Xu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"10.1016\/j.image.2026.117578_b81","first-page":"2716","article-title":"Oriented object detection in remote sensing images based on feature recombination and self-attention","volume":"27","author":"Min","year":"2024","journal-title":"J. Remote. Sens."},{"key":"10.1016\/j.image.2026.117578_b82","doi-asserted-by":"crossref","unstructured":"T.Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.image.2026.117578_b83","doi-asserted-by":"crossref","unstructured":"X. Xie, G. Cheng, J. Wang, X. Yao, J. Han, Oriented R-CNN for object detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 3520\u20133529.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"10.1016\/j.image.2026.117578_b84","first-page":"1","article-title":"S2A-net: Scale-aware attention network for UAV-based tiny object detection","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.image.2026.117578_b85","first-page":"1","article-title":"Dual-aligned oriented detector","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"10.1016\/j.image.2026.117578_b86","first-page":"4932","article-title":"Multi-oriented object detection in aerial images with double horizontal rectangles","volume":"45","author":"Nie","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.image.2026.117578_b87","first-page":"2716","article-title":"Feature reorganization and self-attention for oriented object detection in remote sensing images","volume":"27","author":"Min","year":"2024","journal-title":"J. Remote. Sens."}],"container-title":["Signal Processing: Image Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923596526001013?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923596526001013?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:47:20Z","timestamp":1779878840000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0923596526001013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":87,"alternative-id":["S0923596526001013"],"URL":"https:\/\/doi.org\/10.1016\/j.image.2026.117578","relation":{},"ISSN":["0923-5965"],"issn-type":[{"value":"0923-5965","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DARE-YOLO: A small object detection network for UAV scenarios via feature enhancement and dynamic multi-scale fusion","name":"articletitle","label":"Article Title"},{"value":"Signal Processing: Image Communication","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.image.2026.117578","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"117578"}}