{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T16:17:31Z","timestamp":1789057051600,"version":"build-2803163510"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62005269"],"award-info":[{"award-number":["62005269"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023C031-6"],"award-info":[{"award-number":["2023C031-6"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"2023 Jilin Province industrialization project for the specialized program","award":["62005269"],"award-info":[{"award-number":["62005269"]}]},{"name":"2023 Jilin Province industrialization project for the specialized program","award":["2023C031-6"],"award-info":[{"award-number":["2023C031-6"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection in remote sensing plays a crucial role in various ground identification tasks. However, due to the limited feature information contained within small targets, which are more susceptible to being buried by complex backgrounds, especially in extreme environments (e.g., low-light, motion-blur scenes). Meanwhile, event cameras offer a unique paradigm with high temporal resolution and wide dynamic range for object detection. These advantages enable event cameras without being limited by the intensity of light, to perform better in challenging conditions compared to traditional cameras. In this work, we introduce the Multi-Vision Transformer (MVT), which comprises three efficiently designed components: the downsampling module, the Channel Spatial Attention (CSA) module, and the Global Spatial Attention (GSA) module. This architecture simultaneously considers short-term and long-term dependencies in semantic information, resulting in improved performance for small object detection. Additionally, we propose Cross Deformable Attention (CDA), which progressively fuses high-level and low-level features instead of considering all scales at each layer, thereby reducing the computational complexity of multi-scale features. Nevertheless, due to the scarcity of event camera remote sensing datasets, we provide the Event Object Detection (EOD) dataset, which is the first dataset that includes various extreme scenarios specifically introduced for remote sensing using event cameras. Moreover, we conducted experiments on the EOD dataset and two typical unmanned aerial vehicle remote sensing datasets (VisDrone2019 and UAVDT Dataset). The comprehensive results demonstrate that the proposed MVT-Net achieves a promising and competitive performance.<\/jats:p>","DOI":"10.3390\/rs16091641","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T13:27:00Z","timestamp":1715002020000},"page":"1641","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["MVT: Multi-Vision Transformer for Event-Based Small Target Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8958-4571","authenticated-orcid":false,"given":"Shilong","family":"Jing","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengyi","family":"Lv","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Sun","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/JSSC.2014.2342715","article-title":"A 240\u00d7 180 130 db 3 \u03bcs latency global shutter spatiotemporal vision sensor","volume":"49","author":"Brandli","year":"2014","journal-title":"IEEE J.-Solid-State Circuits"},{"key":"ref_2","unstructured":"Delbruck, T. (2008, January 6\u20137). Frame-free dynamic digital vision. Proceedings of the International Symposium on Secure-Life Electronics, Advanced Electronics for Quality Life and Society, Tokyo, Japan."},{"key":"ref_3","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_4","unstructured":"Du, D., Zhu, P., Wen, L., Bian, X., Lin, H., Hu, Q., Peng, T., Zheng, J., Wang, X., and Zhang, Y. (November, January 27). VisDrone-DET2019: The vision meets drone object detection in image challenge results. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., and Tian, Q. (2018, January 8\u201314). The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 11\u201314). Stacked hourglass networks for human pose estimation. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part VIII 14.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4341","DOI":"10.1109\/TIP.2023.3297408","article-title":"Centralized feature pyramid for object detection","volume":"32","author":"Quan","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","unstructured":"Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., Yu, G., and Shen, C. (November, January 27). Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.isprsjprs.2020.07.005","article-title":"Fully convolutional networks for land cover classification from historical panchromatic aerial photographs","volume":"167","author":"Mboga","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1080\/17538947.2023.2210312","article-title":"Strategies in training deep learning models to extract building from multisource images with small training sample sizes","volume":"16","author":"Abriha","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.03.017","article-title":"Deforestation detection using a spatio-temporal deep learning approach with synthetic aperture radar and multispectral images","volume":"199","author":"Mas","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, C., Xiao, B., Gao, C., Yuan, L., Zhang, L., Sang, N., and Wang, J. (2021, January 19\u201325). Lite-hrnet: A lightweight high-resolution network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01030"},{"key":"ref_15","first-page":"4411915","article-title":"Feature-selection high-resolution network with hypersphere embedding for semantic segmentation of VHR remote sensing images","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hao, X., Yin, L., Li, X., Zhang, L., and Yang, R. (2023). A Multi-Objective Semantic Segmentation Algorithm Based on Improved U-Net Networks. Remote Sens., 15.","DOI":"10.3390\/rs15071838"},{"key":"ref_17","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108962","DOI":"10.1016\/j.sigpro.2023.108962","article-title":"YOLOSR-IST: A deep learning method for small target detection in infrared remote sensing images based on super-resolution and YOLO","volume":"208","author":"Li","year":"2023","journal-title":"Signal Process."},{"key":"ref_19","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_21","first-page":"5001714","article-title":"Dim2Clear network for infrared small target detection","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_24","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_26","unstructured":"Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., and Li, Y. (2022). European Conference on Computer Vision, Springer."},{"key":"ref_27","unstructured":"Jocher, G., Stoken, A., Borovec, J., Changyu, L., Hogan, A., Diaconu, L., Poznanski, J., Yu, L., Rai, P., and Ferriday, R. (2020). ultralytics\/yolov5: v3. 0. Zenodo."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 15\u201320). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_30","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). European Conference on Computer Vision, Springer."},{"key":"ref_31","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.Y. (2022). Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gehrig, M., and Scaramuzza, D. (2023, January 18\u201322). Recurrent vision transformers for object detection with event cameras. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01334"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Iacono, M., Weber, S., Glover, A., and Bartolozzi, C. (2018, January 1\u20135). Towards event-driven object detection with off-the-shelf deep learning. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594119"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Xia, P., Huang, K., Stechele, W., Chen, G., Bing, Z., and Knoll, A. (2019, January 20\u201324). Mixed frame-\/event-driven fast pedestrian detection. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793924"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Su, Q., Chou, Y., Hu, Y., Li, J., Mei, S., Zhang, Z., and Li, G. (2023, January 2\u20136). Deep directly-trained spiking neural networks for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00603"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., and Daniilidis, K. (2019, January 15\u201320). Unsupervised event-based learning of optical flow, depth, and egomotion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00108"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2022.06.002","article-title":"Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark","volume":"190","author":"Xu","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, C., Yang, T., Zhu, S., Chen, C., and Guan, S. (2020, January 14\u201319). Density map guided object detection in aerial images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00103"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 19\u201325). Sparse r-cnn: End-to-end object detection with learnable proposals. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"ref_44","unstructured":"Yang, F., Fan, H., Chu, P., Blasch, E., and Ling, H. (November, January 27). Clustered object detection in aerial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.neucom.2020.06.011","article-title":"Novel up-scale feature aggregation for object detection in aerial images","volume":"411","author":"Lin","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., and Sunderhauf, N. (2021, January 19\u201325). Varifocalnet: An iou-aware dense object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4704314","DOI":"10.1109\/TGRS.2023.3298852","article-title":"Scale decoupled pyramid for object detection in aerial images","volume":"61","author":"Ma","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1109\/TIP.2020.3045636","article-title":"A global-local self-adaptive network for drone-view object detection","volume":"30","author":"Deng","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","unstructured":"Xu, J., Li, Y., and Wang, S. (2021). Adazoom: Adaptive zoom network for multi-scale object detection in large scenes. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1109\/LSP.2022.3229638","article-title":"Zoom-and-reasoning: Joint foreground zoom and visual-semantic reasoning detection network for aerial images","volume":"29","author":"Ge","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yang, X., He, W., Ren, J., Zhang, Q., Zhao, T., Bai, R., He, X., and Liu, J. (2023). Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery. arXiv.","DOI":"10.1609\/aaai.v38i1.27795"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1641\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:39:42Z","timestamp":1760107182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1641"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,4]]},"references-count":52,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091641"],"URL":"https:\/\/doi.org\/10.3390\/rs16091641","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,4]]}}}