{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T22:17:03Z","timestamp":1773872223684,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"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":["61573183"],"award-info":[{"award-number":["61573183"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011222","name":"National Laboratory of Pattern Recognition","doi-asserted-by":"publisher","award":["201900029"],"award-info":[{"award-number":["201900029"]}],"id":[{"id":"10.13039\/501100011222","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once) -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.<\/jats:p>","DOI":"10.3390\/rs13071311","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:13:10Z","timestamp":1617149590000},"page":"1311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection"],"prefix":"10.3390","volume":"13","author":[{"given":"Danqing","family":"Xu","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]},{"given":"Yiquan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.aej.2020.05.035","article-title":"A multispectral feature fusion network for robust pedestrian detection","volume":"60","author":"Song","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ma, J., Wan, H.L., Wang, J.X., Xia, H., and Bai, C.J. (2021). An improved one-stage pedestrian detection method based on multi-scale attention feature extraction. J. Real-Time Image Process.","DOI":"10.1007\/s11554-021-01074-2"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"551","DOI":"10.32604\/csse.2021.014495","article-title":"Efficient Anti-Glare Ceramic Decals Defect Detection by Incorporating Homomorphic Filtering","volume":"36","author":"Chen","year":"2021","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3506","DOI":"10.1109\/TIE.2020.2982115","article-title":"FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile","volume":"68","author":"Xie","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ni, X.C., Dong, G.Y., Li, L.G., Yang, Q.F., and Wu, Z.J. (2021). Kinetic study of electron transport behaviors used for ion sensing technology in air\/ EGR diluted methane flames. Fuel, 288.","DOI":"10.1016\/j.fuel.2020.119825"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alsaadi, H.I.H., Almuttari, R.M., Ucan, O.N., and Bayat, O. (2021). An adapting soft computing model for intrusion detection system. Comput. Intell.","DOI":"10.1111\/coin.12433"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lee, J., Moon, S., Nam, D.W., Lee, J., Oh, A.R., and Yoo, W. (2020, January 21\u201323). A Study on the Identification of Warship Type\/Class by Measuring Similarity with Virtual Warship. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC49870.2020.9289556"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cho, S., Shin, W., Kim, N., Jeong, J., and In, H.P. (2020). Priority Determination to Apply Artificial Intelligence Technology in Military Intelligence Areas. Electronics, 9.","DOI":"10.3390\/electronics9122187"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fukuda, G., Hatta, D., Guo, X., and Kubo, N. (2021). Performance Evaluation of IMU and DVL Integration in Marine Navigation. Sensors, 21.","DOI":"10.3390\/s21041056"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s12942-021-00259-z","article-title":"Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements","volume":"20","author":"Ajayakumar","year":"2021","journal-title":"Int. J. Health Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Muller, D., and Kramer, F. (2021). MIScnn: A framework for medical image segmentation with convolutional neural networks and deep learning. BMC Med. Imaging, 21.","DOI":"10.1186\/s12880-020-00543-7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3090","DOI":"10.1080\/01431161.2020.1864060","article-title":"Small sample classification of hyperspectral image using model-agnostic meta-learning algorithm and convolutional neural network","volume":"42","author":"Gao","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision, (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_15","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":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K.M., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110227","DOI":"10.1109\/ACCESS.2020.3001279","article-title":"Data-Driven Based Tiny-YOLOv3 Method for Front Vehicle Detection Inducing SPP-Net","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"177009","DOI":"10.1109\/ACCESS.2019.2957148","article-title":"High-Resolution SAR Change Detection Based on ROI and SPP Net","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K.M., 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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_21","first-page":"239","article-title":"Parallel Feature Pyramid Network for Object Detection","volume":"Volume 11209","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018, Proceedings of the 15th European Conference on Computer Vision, Munich, Germany, 8\u201314 September 2018"},{"key":"ref_22","unstructured":"Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., and Garnett, R. (2016). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Advances in Neural Information Processing Systems 29, Procedings of the 30th Annual Conference on Neural Information Processing Systems, Barcelona, Spain, 5\u201310 December 2016, Curran Associates, Inc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Singh, B., and Davis, L.S. (2018, January 18\u201323). An Analysis of Scale Invariance in Object Detection\u2014SNIP. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"ref_24","unstructured":"Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa Bianchi, N., and Garnett, R. (2019). SNIPER: Efficient Multi-Scale Training. Advances in Neural Information Processing Systems 31, Proceedings of the Annual Conference on Neural Information Processing Systems, Montr\u00e9al, Canada, 3\u20138 December 2018, Curran Associates, Inc."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_27","unstructured":"Redmon, J., and Farhadi, A.J. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Adarsh, P., Rathi, P., and Kumar, M. (2020, January 6\u20137). YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems, Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"ref_29","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_30","first-page":"21","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Leibe","year":"2016","journal-title":"Computer Vision\u2014Eccv 2016, Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 8\u201316 October 2016"},{"key":"ref_31","unstructured":"Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). DSSD: Deconvolutional Single Shot Detector. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"188577","DOI":"10.1109\/ACCESS.2020.3031990","article-title":"A Lightweight Feature Fusion Single Shot Multibox Detector for Garbage Detection","volume":"8","author":"Ma","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, T., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., and Shao, L. (November, January 27). Learning Rich Features at High-Speed for Single-Shot Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00206"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2020.10.039","article-title":"Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty","volume":"292","author":"Shone","year":"2021","journal-title":"Eur. J. Oper. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/LRA.2021.3056344","article-title":"iCurb: Imitation Learning-Based Detection of Road Curbs Using Aerial Images for Autonomous Driving","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ji, X., Yan, Q., Huang, D., Wu, B., Xu, X., Zhang, A., Liao, G., Zhou, J., and Wu, M. (2021). Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition. J. Mater. Process. Technol., 292.","DOI":"10.1016\/j.jmatprotec.2021.117064"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1007\/s42835-020-00617-0","article-title":"H-infinity Approach to Performance Analysis of Missile Control Systems with Proportional Navigation Guidance Laws","volume":"16","author":"Song","year":"2021","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_38","first-page":"364","article-title":"Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net","volume":"Volume 10635","author":"Liu","year":"2017","journal-title":"Neural Information Processing, Proceedings of the International Conference on Neural Information Processing, Guangzhou, China, 14\u201318 November 2017"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Oyama, T., and Yamanaka, T. (2017, January 26\u201329). Fully Convolutional DenseNet for Saliency-Map Prediction. Proceedings of the 4th IAPR Asian Conference on Pattern Recognition, Nanjing, China.","DOI":"10.1109\/ACPR.2017.143"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., and Jiao, J. (2015, January 27\u201330). ORIENTATION ROBUST OBJECT DETECTION IN AERIAL IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK. Proceedings of the 2015 IEEE International Conference on Image Processing, Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","article-title":"Vehicle detection in aerial imagery: A small target detection benchmark","volume":"34","author":"Razakarivony","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, M.J., Wang, X.H., Zhou, A.J., Fu, X.Y., Ma, Y.W., and Piao, C.H. (2020). UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors, 20.","DOI":"10.3390\/s20082238"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ins.2020.02.067","article-title":"DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection","volume":"522","author":"Huang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_45","unstructured":"Zhang, S., Mu, X., Kou, G., and Zhao, J. (2020). Object Detection Based on Efficient Multiscale Auto-Inference in Remote Sensing Images. IEEE Geosci. Remote. Sens. Lett., 1\u20135."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"012084","DOI":"10.1088\/1742-6596\/1325\/1\/012084","article-title":"Vehicle and Parking Space Detection Based on Improved YOLO Network Model","volume":"1325","author":"Ding","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"He, W., Huang, Z., Wei, Z., Li, C., and Guo, B. (2019). TF-YOLO: An Improved Incremental Network for Real-Time Object Detection. Appl. Sci., 9.","DOI":"10.3390\/app9163225"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wu, X., Zheng, G., and Liu, X. (2019, January 27\u201330). Object Detection of UAV for Anti-UAV Based on Improved YOLO v3. Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China.","DOI":"10.23919\/ChiCC.2019.8865525"},{"key":"ref_49","unstructured":"Long, X., Deng, K., Wang, G., Zhang, Y., and Wen, S. (2020). PP-YOLO: An Effective and Efficient Implementation of Object Detector. arXiv."},{"key":"ref_50","unstructured":"Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., and Feng, J. (2017). Dual Path Networks. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1311\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:07Z","timestamp":1760362387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,30]]},"references-count":50,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071311"],"URL":"https:\/\/doi.org\/10.3390\/rs13071311","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,30]]}}}