{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:23:51Z","timestamp":1768350231584,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62001350"],"award-info":[{"award-number":["62001350"]}]},{"name":"National Natural Science Foundation of China","award":["2016M602775"],"award-info":[{"award-number":["2016M602775"]}]},{"name":"National Natural Science Foundation of China","award":["2018BSHEDZZ39"],"award-info":[{"award-number":["2018BSHEDZZ39"]}]},{"name":"National Natural Science Foundation of China","award":["6141A02022367"],"award-info":[{"award-number":["6141A02022367"]}]},{"name":"National Natural Science Foundation of China","award":["XJS210210"],"award-info":[{"award-number":["XJS210210"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62001350"],"award-info":[{"award-number":["62001350"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016M602775"],"award-info":[{"award-number":["2016M602775"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018BSHEDZZ39"],"award-info":[{"award-number":["2018BSHEDZZ39"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["6141A02022367"],"award-info":[{"award-number":["6141A02022367"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["XJS210210"],"award-info":[{"award-number":["XJS210210"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Science Research Projects of Shaanxi Province","award":["62001350"],"award-info":[{"award-number":["62001350"]}]},{"name":"Postdoctoral Science Research Projects of Shaanxi Province","award":["2016M602775"],"award-info":[{"award-number":["2016M602775"]}]},{"name":"Postdoctoral Science Research Projects of Shaanxi Province","award":["2018BSHEDZZ39"],"award-info":[{"award-number":["2018BSHEDZZ39"]}]},{"name":"Postdoctoral Science Research Projects of Shaanxi Province","award":["6141A02022367"],"award-info":[{"award-number":["6141A02022367"]}]},{"name":"Postdoctoral Science Research Projects of Shaanxi Province","award":["XJS210210"],"award-info":[{"award-number":["XJS210210"]}]},{"name":"Joint Fund of Ministry of Education","award":["62001350"],"award-info":[{"award-number":["62001350"]}]},{"name":"Joint Fund of Ministry of Education","award":["2016M602775"],"award-info":[{"award-number":["2016M602775"]}]},{"name":"Joint Fund of Ministry of Education","award":["2018BSHEDZZ39"],"award-info":[{"award-number":["2018BSHEDZZ39"]}]},{"name":"Joint Fund of Ministry of Education","award":["6141A02022367"],"award-info":[{"award-number":["6141A02022367"]}]},{"name":"Joint Fund of Ministry of Education","award":["XJS210210"],"award-info":[{"award-number":["XJS210210"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62001350"],"award-info":[{"award-number":["62001350"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2016M602775"],"award-info":[{"award-number":["2016M602775"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2018BSHEDZZ39"],"award-info":[{"award-number":["2018BSHEDZZ39"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["6141A02022367"],"award-info":[{"award-number":["6141A02022367"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["XJS210210"],"award-info":[{"award-number":["XJS210210"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&amp;C) signals often become submerged in background noise. Traditional TT&amp;C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&amp;C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&amp;C signals with signal-to-noise ratios (SNRs) of \u221215 dB and above (mAP@0.5 &gt; 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.<\/jats:p>","DOI":"10.3390\/rs16061008","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:08:43Z","timestamp":1710335323000},"page":"1008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent Detection Method for Satellite TT&amp;C Signals under Restricted Conditions Based on TATR"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6951-083X","authenticated-orcid":false,"given":"Yu","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4636-2966","authenticated-orcid":false,"given":"Xiaoran","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xiaoning","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yongqiang","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Astronautic Dynamics, Xi\u2019an 710043, China"}]},{"given":"Peipei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1514-7393","authenticated-orcid":false,"given":"Feng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Pan, J. (2023). Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area. Remote Sens., 15.","DOI":"10.3390\/rs15225387"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, W., Sun, Y., Bai, W., Du, Q., Wang, X., Wang, D., Liu, C., Li, F., Kang, S., and Song, H. (2024). A Novel Approach to Evaluate GNSS-RO Signal Receiver Performance in Terms of Ground-Based Atmospheric Occultation Simulation System. Remote Sens., 16.","DOI":"10.3390\/rs16010087"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5235415","DOI":"10.1109\/TGRS.2022.3208333","article-title":"Electromagnetic Scattering Feature (ESF) Module Embedded Network Based on ASC Model for Robust and Interpretable SAR ATR","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"5204617","article-title":"PAN: Part Attention Network Integrating Electromagnetic Characteristics for Interpretable SAR Vehicle Target Recognition","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8950","DOI":"10.1109\/TVT.2021.3098710","article-title":"Weak Signal Frequency Detection Using Chaos Theory: A Comprehensive Analysis","volume":"70","author":"Chen","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1109\/LSP.2020.2996091","article-title":"High-Precision Trajectory Data Reconstruction for TT&C Systems Using LS B-Spline Approximation","volume":"27","author":"Sun","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9421","DOI":"10.1109\/TVT.2018.2854730","article-title":"Soft-Feedback Time-Domain Turbo Equalization for Single-Carrier Generalized Spatial Modulation","volume":"67","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"144134","DOI":"10.1109\/ACCESS.2019.2945834","article-title":"Improvement of Non-Maximum Suppression in RGB-D Object Detection","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1109\/TIP.2023.3268561","article-title":"Neural Attention-Driven Non-Maximum Suppression for Person Detection","volume":"32","author":"Symeonidis","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A.U., and Hou, Q. (2021, January 5\u20139). Rotate to Attend: Convolutional Triplet Attention Module. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Stewart, R., Andriluka, M., and Ng, A.Y. (2016, January 26\u201330). End-To-End People Detection in Crowded Scenes. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.255"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/LSP.2019.2900165","article-title":"Energy Detection Scheme in the Presence of Burst Signals","volume":"26","author":"Oh","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TSP.2007.909322","article-title":"Nonparametric Detection of FM Signals Using Time-Frequency Ridge Energy","volume":"56","author":"Shui","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7747","DOI":"10.1109\/TVT.2019.2923648","article-title":"Maximum Eigenvalue-Based Goodness-of-Fit Detection for Spectrum Sensing in Cognitive Radio","volume":"68","author":"Liu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/LGRS.2014.2355915","article-title":"A Geometric Matched Filter for Hyperspectral Target Detection and Partial Unmixing","volume":"12","author":"Akhter","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/LGRS.2005.857619","article-title":"Effect of Signal Contamination in Matched-filter Detection of the Signal on a Cluttered Background","volume":"3","author":"Theiler","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TSP.2009.2029790","article-title":"Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio","volume":"58","author":"Lunden","year":"2010","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/LCOMM.2012.100812.122009","article-title":"A Cyclic Correlation-Based Blind SINR Estimation for OFDM Systems","volume":"16","author":"Hong","year":"2012","journal-title":"IEEE Commun. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6305","DOI":"10.1109\/ACCESS.2023.3237396","article-title":"Energy Detection for M-QAM Signals","volume":"11","author":"Ishihara","year":"2023","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/LCOMM.2022.3161058","article-title":"Linearized Model for MIMO-MFSK Systems with Energy Detection","volume":"26","author":"Zheng","year":"2022","journal-title":"IEEE Commun. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_23","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","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. arXiv.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_27","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable Bag-of-freebies Sets New State-of-the-art for Real-time Object Detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/LSP.2022.3179958","article-title":"A New Deep Learning Framework for HF Signal Detection in Wideband Spectrogram","volume":"29","author":"Li","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Shi, X., Yang, X., and Zhou, F. (2023, January 14\u201317). Unsupervised Modulation Recognition Method Based on Multi-Domain Representation Contrastive Learning. Proceedings of the 2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Zhengzhou, China.","DOI":"10.1109\/ICSPCC59353.2023.10400274"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21574","DOI":"10.1109\/JSEN.2023.3303023","article-title":"An Incremental Recognition Method for MFR Working Modes Based on Deep Feature Extension in Dynamic Observation Scenarios","volume":"23","author":"Zhang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"89218","DOI":"10.1109\/ACCESS.2019.2926296","article-title":"Blind Detection Techniques for Non-Cooperative Communication Signals Based on Deep Learning","volume":"7","author":"Ke","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Prasad, K.N.R.S.V., Dsouza, K.B., Bhargava, V.K., Mallick, S., and Boostanimehr, H. (2020, January 25\u201328). A Deep Learning Framework for Blind Time-Frequency Localization in Wideband Systems. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium.","DOI":"10.1109\/VTC2020-Spring48590.2020.9128779"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, W., Ma, W., Wang, S., Gu, X., Ni, B., Cheng, W., Feng, J., Wang, Q., and Hu, M. (2023). Automatic Detection of VLF Tweek Signals Based on the YOLO Model. Remote Sens., 15.","DOI":"10.3390\/rs15205019"},{"key":"ref_35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2023). Attention Is All You Need. arXiv."},{"key":"ref_36","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-End Object Detection with Transformers. Proceedings of the 2020 European Conference on Computer Vision (ECCV), Online.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_38","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., and Dai, J. (2020). Deformable DETR: Deformable Transformers for End-to-End Object Detection. arXiv."},{"key":"ref_39","first-page":"2567","article-title":"Anchor DETR: Query Design for Transformer-Based Detector","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"183488","DOI":"10.1109\/ACCESS.2020.3028367","article-title":"2D-HRA: Two-Dimensional Hierarchical Ring-Based All-Reduce Algorithm in Large-Scale Distributed Machine Learning","volume":"8","author":"Jiang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Fu, F., Miao, X., Nie, X., and Cui, B. (2023). OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning. arXiv.","DOI":"10.24963\/ijcai.2023\/238"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhu, J., Geng, J., Deng, X., and Jiang, W. (2021, January 11\u201316). Triplet Attention Feature Fusion Network for SAR and Optical Image Land Cover Classification. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9555126"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/COMST.2023.3287431","article-title":"A Tutorial on the Tracking, Telemetry, and Command (TT&C) for Space Missions","volume":"25","author":"Modenini","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., and Yang, Q. (2023). Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture. Remote Sens., 15.","DOI":"10.3390\/rs15061695"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 26\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_47","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 2017 International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yu, C., Feng, Z., Wu, Z., Wei, R., Song, B., and Cao, C. (2023). HB-YOLO: An Improved YOLOv7 Algorithm for Dim-Object Tracking in Satellite Remote Sensing Videos. Remote Sens., 15.","DOI":"10.3390\/rs15143551"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/6\/1008\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:12:53Z","timestamp":1760105573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/6\/1008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,13]]},"references-count":51,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16061008"],"URL":"https:\/\/doi.org\/10.3390\/rs16061008","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,13]]}}}