{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T02:18:09Z","timestamp":1775787489280,"version":"3.50.1"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR2020KE023 and ZR2021MD057"],"award-info":[{"award-number":["ZR2020KE023 and ZR2021MD057"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image background, e.g., hiding in front of a tree. Furthermore, video-based detection systems heavily rely on the image\u2019s brightness, where darkness imposes significant challenges in detecting drones. Both these phenomena increase the possibilities for attackers to get close to critical infrastructures without being spotted and hence be able to gather sensitive information or cause physical damages, possibly leading to safety threats.<\/jats:p>\n          <jats:p>In this article, we propose RANGO, a drone detection arithmetic able to detect drones in challenging images where the target is difficult to distinguish from the background. RANGO is based on a deep learning architecture that exploits a Preconditioning Operation (PREP) that highlights the target by the difference between the target gradient and the background gradient. The idea is to highlight features that will be useful for classification. After PREP, RANGO uses multiple convolution kernels to make the final decision on the presence of the drone. We test RANGO on a drone image dataset composed of multiple already-existing datasets to which we add samples of birds and planes. We then compare RANGO with multiple currently existing approaches to show its superiority. When tested on images with disguising drones, RANGO attains an increase of 6.6% mean Average Precision (mAP) compared to YOLOv5 solution. When tested on the conventional dataset, RANGO improves the mAP by approximately 2.2%, thus confirming its effectiveness also in the general scenario.<\/jats:p>","DOI":"10.1145\/3641282","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T12:28:28Z","timestamp":1706012908000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9330-5019","authenticated-orcid":false,"given":"Jin","family":"Han","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8862-9598","authenticated-orcid":false,"given":"Yun-Feng","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6138-2995","authenticated-orcid":false,"given":"Alessandro","family":"Brighente","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Padova, Padua, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3612-1934","authenticated-orcid":false,"given":"Mauro","family":"Conti","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Padova, Padua, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3147063"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11244203"},{"key":"e_1_3_1_4_2","unstructured":"arstechnica. 2013. German Chancellor\u2019s Drone \u201cattack\u201d Shows the Threat of Weaponized UAVs. Retrieved from https:\/\/arstechnica.com\/information-technology\/2013\/09\/german-chancellors-drone-attack-shows-the-threat-of-weaponized-uavs\/"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2019.01.012"},{"key":"e_1_3_1_6_2","first-page":"2654","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Ba Jimmy","year":"2014","unstructured":"Jimmy Ba and Rich Caruana. 2014. Do deep nets really need to be deep? In Annual Conference on Neural Information Processing Systems, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 2654\u20132662."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICICCS48265.2020.9121150"},{"key":"e_1_3_1_8_2","article-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"Bochkovskiy Alexey","year":"2020","unstructured":"Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020).","journal-title":"arXiv preprint arXiv:2004.10934"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/APSIPA.2017.8282120"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1974.1100577"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.297033"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_3_1_15_2","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy Alexey","year":"2020","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et\u00a0al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).","journal-title":"arXiv preprint arXiv:2010.11929"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01161"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00667"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2019.2955889"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3154474"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3161527"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3142949"},{"key":"e_1_3_1_22_2","unstructured":"Haibo He and Yunqian Ma. 2013. Imbalanced learning: Foundations algorithms and applications. Wiley-IEEE Press 1 27 (2013) 12."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_3_1_26_2","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).","journal-title":"arXiv preprint arXiv:1704.04861"},{"key":"e_1_3_1_27_2","series-title":"32nd International Conference on Machine Learning (ICML\u201915)","first-page":"448","volume":"37","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In 32nd International Conference on Machine Learning (ICML\u201915)(JMLR Workshop and Conference Proceedings, Vol. 37), Francis R. Bach and David M. Blei (Eds.). JMLR.org, 448\u2013456."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3174583"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3193775"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.3908559"},{"key":"e_1_3_1_31_2","volume-title":"YOLO by Ultralytics","author":"Jocher Glenn","year":"2023","unstructured":"Glenn Jocher, Ayush Chaurasia, and Jing Qiu. 2023. YOLO by Ultralytics. Retrieved from https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103653"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3159238"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC51459.2021.9415217"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/RADAR.2017.7944443"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3041038"},{"key":"e_1_3_1_39_2","article-title":"SoK-security and privacy in the age of drones: Threats, challenges, solution mechanisms, and scientific gaps","author":"Nassi Ben","year":"2019","unstructured":"Ben Nassi, Asaf Shabtai, Ryusuke Masuoka, and Yuval Elovici. 2019. SoK-security and privacy in the age of drones: Threats, challenges, solution mechanisms, and scientific gaps. arXiv preprint arXiv:1903.05155 (2019).","journal-title":"arXiv preprint arXiv:1903.05155"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3065926"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3026192"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ATC52653.2021.9598317"},{"key":"e_1_3_1_43_2","volume-title":"Conference on Computer Vision and Pattern Recognition (CVPR\u201921)","author":"Piao Zhixin","year":"2021","unstructured":"Zhixin Piao, Yongyun Jin, Stephen Li, Chunhua Shen, and Anton van den Hengel. 2021. Vision transformer for large-scale aerial image segmentation. In Conference on Computer Vision and Pattern Recognition (CVPR\u201921)."},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1111\/1745-9133.12419"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.315601"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9413241"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023052124951"},{"key":"e_1_3_1_53_2","first-page":"5998","volume-title":"Annual Conference on Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Annual Conference on Neural Information Processing Systems, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998\u20136008."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2001.990517"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196947"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.08.010"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3056059"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00312"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641282","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3641282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:48Z","timestamp":1750295868000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,22]]},"references-count":61,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4,30]]}},"alternative-id":["10.1145\/3641282"],"URL":"https:\/\/doi.org\/10.1145\/3641282","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,22]]},"assertion":[{"value":"2022-12-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-02","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}