{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T11:43:01Z","timestamp":1781264581508,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["41876105"],"award-info":[{"award-number":["41876105"]}]},{"name":"National Science Foundation of China","award":["41671410"],"award-info":[{"award-number":["41671410"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object detection is used widely in remote sensing image interpretation. Although most models used for object detection have achieved high detection accuracy, computational complexity and low detection speeds limit their application in real-time detection tasks. This study developed an adaptive feature-aware method of object detection in remote sensing images based on the single-shot detector architecture called adaptive feature-aware detector (AFADet). Self-attention is used to extract high-level semantic information derived from deep feature maps for spatial localization of objects and the model is improved in localizing objects. The adaptive feature-aware module is used to perform adaptive cross-scale depth fusion of different-scale feature maps to improve the learning ability of the model and reduce the influence of complex backgrounds in remote sensing images. The focal loss is used during training to address the positive and negative sample imbalance problem, reduce the influence of the loss value dominated by easily classified samples, and enhance the stability of model training. Experiments are conducted on three object detection datasets, and the results are compared with those of the classical and recent object detection algorithms. The mean average precision(mAP) values are 66.12%, 95.54%, and 86.44% for three datasets, which suggests that AFADet can detect remote sensing images in real-time with high accuracy and can effectively balance detection accuracy and speed.<\/jats:p>","DOI":"10.3390\/rs14153616","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T22:43:26Z","timestamp":1659048206000},"page":"3616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Object Detection Based on Adaptive Feature-Aware Method in Optical Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiaqi","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihui","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5858-7671","authenticated-orcid":false,"given":"Donghang","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/LGRS.2020.2975541","article-title":"Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images","volume":"18","author":"Cheng","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","first-page":"1","article-title":"ASSD: Feature Aligned Single-Shot Detection for Multiscale Objects in Aerial Imagery","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"1","article-title":"A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images","volume":"60","author":"Huang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qian, X., Lin, S., Cheng, G., Yao, X., Ren, H., and Wang, W. (2020). Object Detection in Remote Sensing Images Based on Improved Bounding Box Regression and Multi-Level Features Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12010143"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J.Y., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9805389","DOI":"10.34133\/2021\/9805389","article-title":"Feature enhancement network for object detection in optical remote sensing images","volume":"2021","author":"Cheng","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","article-title":"Oriented objects as pairs of middle lines","volume":"169","author":"Wei","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, W., Li, G., Chen, Q., Ju, M., and Qu, J. (2021). CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection. Remote Sens., 13.","DOI":"10.3390\/rs13050847"},{"key":"ref_9","first-page":"1","article-title":"FSoD-Net: Full-scale object detection from optical remote sensing imagery","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","first-page":"1","article-title":"Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","first-page":"1","article-title":"LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images","volume":"60","author":"Huang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"6001605","article-title":"Efficient Detection in Aerial Images for Resource-Limited Satellites","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12557","DOI":"10.1109\/JSTARS.2021.3128566","article-title":"AFDet: Toward More Accurate and Faster Object Detection in Remote Sensing Images","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, P., and Che, C. (2021, January 18\u201322). SeMo-YOLO: A multiscale object detection network in satellite remote sensing images. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534343"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lang, L., Xu, K., Zhang, Q., and Wang, D. (2021). Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network. Sensors, 21.","DOI":"10.3390\/s21165460"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, 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_18","first-page":"1","article-title":"ABNet: Adaptive Balanced Network for Multiscale Object Detection in Remote Sensing Imagery","volume":"60","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ye, Y., Ren, X., Zhu, B., Tang, T., Tan, X., Gui, Y., and Yao, Q. (2022). An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14030516"},{"key":"ref_20","first-page":"6503305","article-title":"Efficient Vertex Coordinate Prediction-Based CSP-Hourglass Net for Object OBB Detection in Remote Sensing","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","first-page":"1","article-title":"Feature Split\u2013Merge\u2013Enhancement Network for Remote Sensing Object Detection","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"1","article-title":"FRPNet: A Feature-Reflowing Pyramid Network for Object Detection of Remote Sensing Images","volume":"19","author":"Wang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3228927","article-title":"GCWNet: A Global Context-Weaving Network for Object Detection in Remote Sensing Images","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"1","article-title":"Multi-Vision Network for Accurate and Real-Time Small Object Detection in Optical Remote Sensing Images","volume":"19","author":"Han","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, J., Yang, D., and Hu, F. (2022). Multiscale Object Detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention. Remote Sens., 14.","DOI":"10.3390\/rs14020427"},{"key":"ref_26","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 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3123984","article-title":"RRNet: Relational Reasoning Network With Parallel Multiscale Attention for Salient Object Detection in Optical Remote Sensing Images","volume":"60","author":"Cong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"Semantic Context-Aware Network for Multiscale Object Detection in Remote Sensing Images","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040273","article-title":"A New Spatial-Oriented Object Detection Framework for Remote Sensing Images","volume":"60","author":"Yu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"1","article-title":"Foreground Refinement Network for Rotated Object Detection in Remote Sensing Images","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8021","DOI":"10.1109\/ACCESS.2022.3141059","article-title":"Multi-Size Object Detection in Large Scene Remote Sensing Images Under Dual Attention Mechanism","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_32","first-page":"1","article-title":"Object Detection in Large-Scale Remote-Sensing Images Based on Time-Frequency Analysis and Feature Optimization","volume":"60","author":"Bai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1007\/s10489-021-02335-0","article-title":"Spatial hierarchy perception and hard samples metric learning for high-resolution remote sensing image object detection","volume":"52","author":"Zhu","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_34","first-page":"1","article-title":"Target detection in remote sensing image based on object-and-scene context constrained CNN","volume":"19","author":"Cheng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.23915\/distill.00021","article-title":"Computing Receptive Fields of Convolutional Neural Networks","volume":"4","author":"Araujo","year":"2019","journal-title":"Distill"},{"key":"ref_36","unstructured":"Luo, W., Li, Y., Urtasun, R., and Zemel, R. (2016, January 5\u201310). Understanding the effective receptive field in deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 29, Barcelona, Spain."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mei, H., Ji, G.P., Wei, Z., Yang, X., Wei, X., and Fan, D.-P. (2021, January 20\u201325). Camouflaged Object Segmentation with Distraction Mining. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Yan, M., Wang, C., Shi, J., and Zhang, X. (August, January 28). Object Detection and Instance Segmentation in Remote Sensing Imagery Based on Precise Mask R-CNN. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898573"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/01431161.2014.999881","article-title":"Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images","volume":"36","author":"Xiao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, 28, Montreal, QC, Canada."},{"key":"ref_43","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_44","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_45","unstructured":"Van Etten, A. (2018). You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv."},{"key":"ref_46","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_47","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3616\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:58:27Z","timestamp":1760140707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3616"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":47,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153616"],"URL":"https:\/\/doi.org\/10.3390\/rs14153616","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}