{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:09:38Z","timestamp":1782403778909,"version":"3.54.5"},"reference-count":85,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0101200"],"award-info":[{"award-number":["2018AAA0101200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020XD-A09-3"],"award-info":[{"award-number":["2020XD-A09-3"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"crossref","award":["B18008"],"award-info":[{"award-number":["B18008"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972044 and 61732017"],"award-info":[{"award-number":["61972044 and 61732017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funds for International Cooperation and Exchange of NSFC","award":["61720106007"],"award-info":[{"award-number":["61720106007"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,9,6]]},"abstract":"<jats:p>Vision-based drone-view object detection suffers from severe performance degradation under adverse conditions (e.g., foggy weather, poor illumination). To remedy this, leveraging complementary mmWave radar has become a trend. However, existing fusion approaches seldom apply to drones due to i) the aggravated sparsity and noise of point clouds from low-cost commodity radars, and ii) explosive sensing data and intensive computations leading to high latency. To address these issues, we design Geryon, an edge assisted object detection system on drones, which utilizes a suit of approaches to fully exploit the complementary advantages of camera and mmWave radar on three levels: (i) a novel multi-frame compositing approach utilizes camera to assist radar to address the aggravated sparsity and noise of radar point clouds; (ii) a saliency area extraction and encoding approach utilizes radar to assist camera to reduce the bandwidth consumption and offloading latency; (iii) a parallel transmission and inference approach with a lightweight box enhancement scheme further reduces the offloading latency while ensuring the edge-side accuracy-latency trade-off by the parallelism and better camera-radar fusion. We implement and evaluate Geryon with four datasets we collect under foggy\/rainy\/snowy weather and poor illumination conditions, demonstrating its great advantages over other state-of-the-art approaches in terms of both accuracy and latency.<\/jats:p>","DOI":"10.1145\/3550298","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T14:54:27Z","timestamp":1662562467000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Geryon"],"prefix":"10.1145","volume":"6","author":[{"given":"Kaikai","family":"Deng","sequence":"first","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoyue","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anfu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huadong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000011205.11775.fd"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196884"},{"key":"e_1_2_1_3_1","volume-title":"Masking by moving: Learning distraction-free radar odometry from pose information. arXiv preprint arXiv:1909.03752","author":"Barnes Dan","year":"2019","unstructured":"Dan Barnes, Rob Weston, and Ingmar Posner. 2019. Masking by moving: Learning distraction-free radar odometry from pose information. arXiv preprint arXiv:1909.03752 (2019)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00909"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00246"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500543"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01177-1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794312"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICKII51822.2021.9574744"},{"key":"e_1_2_1_11_1","volume-title":"Proc. of ACM SenSys. ACM, 155--168","author":"Yu-Han Chen Tiffany","year":"2015","unstructured":"Tiffany Yu-Han Chen, Lenin Ravindranath, Shuo Deng, Paramvir Bahl, and Hari Balakrishnan. 2015. Glimpse: Continuous, real-time object recognition on mobile devices. In Proc. of ACM SenSys. ACM, 155--168."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.691"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01498"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00255"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3045636"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"e_1_2_1_17_1","volume-title":"Proc. of ACM SIGKDD","volume":"96","author":"Ester Martin","year":"1996","unstructured":"Martin Ester, Hans-Peter Kriegel, J\u00f6rg Sander, Xiaowei Xu, et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of ACM SIGKDD, Vol. 96. ACM, 226--231."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241559"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"e_1_2_1_20_1","volume-title":"Jirka Borovec","author":"Glenn Jocher","year":"2022","unstructured":"Jocher Glenn, Stoken Alex, and et al. Jirka Borovec. 2022. ultralytics\/yolov5. Retrieved 2022 from https:\/\/github.com\/ultralytics\/yolov5"},{"key":"e_1_2_1_21_1","volume-title":"Proc","author":"Godard Cl\u00e9ment","unstructured":"Cl\u00e9ment Godard, Kevin Matzen, and Matt Uyttendaele. 2018. Deep burst denoising. In Proc. of Springer ECCV. Springer, 538--554."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"e_1_2_1_25_1","volume-title":"Proc. of ICLR","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. Proc. of ICLR (2019)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.351"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081360"},{"key":"e_1_2_1_28_1","unstructured":"Texas Instruments. 2020. IWR6843 intelligent mmWave sensor standard antenna plug-in module. Retrieved 2020 from https:\/\/www.ti.com\/tool\/IWR6843ISK"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483274"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155435"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3093337.3037698"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419194"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2867951"},{"key":"e_1_2_1_34_1","volume-title":"What happens for a ToF LiDAR in fog? IEEE Transactions on Intelligent Transportation Systems","author":"Li You","year":"2020","unstructured":"You Li, Pierre Duthon, Mich\u00e8le Colomb, and Javier Ibanez-Guzman. 2020. What happens for a ToF LiDAR in fog? IEEE Transactions on Intelligent Transportation Systems (2020)."},{"key":"e_1_2_1_35_1","volume-title":"Light-head r-cnn: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264","author":"Li Zeming","year":"2017","unstructured":"Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, and Jian Sun. 2017. Light-head r-cnn: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264 (2017)."},{"key":"e_1_2_1_36_1","volume-title":"Proc","author":"Liang Ming","unstructured":"Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun. 2018. Deep continuous fusion for multi-sensor 3d object detection. In Proc. of Springer ECCV. Springer, 641--656."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356508"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340998"},{"key":"e_1_2_1_39_1","volume-title":"Proc","author":"Lin Tsung-Yi","unstructured":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Proc. of Springer ECCV. Springer, 740--755."},{"key":"e_1_2_1_40_1","first-page":"1","article-title":"Pmc: A privacy-preserving deep learning model customization framework for edge computing","volume":"4","author":"Liu Bingyan","year":"2020","unstructured":"Bingyan Liu, Yuanchun Li, Yunxin Liu, Yao Guo, and Xiangqun Chen. 2020. Pmc: A privacy-preserving deep learning model customization framework for edge computing. Proc. of ACM IMWUT 4, 4 (2020), 1--25.","journal-title":"Proc. of ACM IMWUT"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3300116"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210337"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2018.10.010"},{"key":"e_1_2_1_44_1","volume-title":"Proc. of University of California Press Berkeley symposium on mathematical statistics and probability","volume":"1","author":"James","unstructured":"James MacQueen et al. 1967. Some methods for classification and analysis of multivariate observations. In Proc. of University of California Press Berkeley symposium on mathematical statistics and probability, Vol. 1. IEEE, 281--297."},{"key":"e_1_2_1_45_1","volume-title":"Proc. of ResearchGate National Conference in Information Technology Education.","author":"Malinao Ronjie Mar L","year":"2018","unstructured":"Ronjie Mar L Malinao and Alexander A Hernandez. 2018. Potentials of using Unmanned Aerial Vehicle in the Philippine Farming Sector: Empirical evidence from field survey. In Proc. of ResearchGate National Conference in Information Technology Education."},{"key":"e_1_2_1_46_1","unstructured":"Marketsandmarkets. 2019. Unmanned aerial vehicle (UAV) market. Retrieved 2019 from https:\/\/www.marketsandmarkets.com\/Market-Reports\/unmanned-aerial-vehicles-uav-market-662.html"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081359"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5430"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803392"},{"key":"e_1_2_1_50_1","first-page":"1","article-title":"Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery","volume":"5","author":"Pan Yan","year":"2021","unstructured":"Yan Pan, Shining Li, Qianwu Chen, Nan Zhang, Tao Cheng, Zhigang Li, Bin Guo, Qingye Han, and Ting Zhu. 2021. Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery. Proc. of ACM IMWUT 5, 1 (2021), 1--23.","journal-title":"Proc. of ACM IMWUT"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00102"},{"key":"e_1_2_1_52_1","volume-title":"Proc. of IEEE CVPR. IEEE, 652--660","author":"Qi Charles R","year":"2017","unstructured":"Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proc. of IEEE CVPR. IEEE, 652--660."},{"key":"e_1_2_1_53_1","first-page":"1","article-title":"3D point cloud generation with millimeter-wave radar","volume":"4","author":"Qian Kun","year":"2020","unstructured":"Kun Qian, Zhaoyuan He, and Xinyu Zhang. 2020. 3D point cloud generation with millimeter-wave radar. Proc. of ACM IMWUT 4, 4 (2020), 1--23.","journal-title":"Proc. of ACM IMWUT"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00051"},{"key":"e_1_2_1_55_1","first-page":"1","article-title":"SquiggleMilli: Approximating SAR Imaging on Mobile Millimeter-Wave Devices","volume":"5","author":"Regmi Hem","year":"2021","unstructured":"Hem Regmi, Moh Sabbir Saadat, Sanjib Sur, and Srihari Nelakuditi. 2021. SquiggleMilli: Approximating SAR Imaging on Mobile Millimeter-Wave Devices. Proc. of ACM IMWUT 5, 3 (2021), 1--26.","journal-title":"Proc. of ACM IMWUT"},{"key":"e_1_2_1_56_1","first-page":"1","article-title":"Adaptive Computation Offloading for Mobile Augmented Reality","volume":"5","author":"Ren Jie","year":"2021","unstructured":"Jie Ren, Ling Gao, Xiaoming Wang, Miao Ma, Guoyong Qiu, Hai Wang, Jie Zheng, and Zheng Wang. 2021. Adaptive Computation Offloading for Mobile Augmented Reality. Proc. of ACM IMWUT 5, 4 (2021), 1--30.","journal-title":"Proc. of ACM IMWUT"},{"key":"e_1_2_1_57_1","volume-title":"Faster r-cnn: Towards real-time object detection with region proposal networks. Proc","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Proc. of MIT Press NeurIPS 28 (2015), 91--99."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450268.3453532"},{"key":"e_1_2_1_60_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155524"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448625"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414883"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00055"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414686"},{"key":"e_1_2_1_70_1","volume-title":"Proc","author":"Wang Yi","unstructured":"Yi Wang, Youlong Yang, and Xi Zhao. 2020. Object detection using clustering algorithm adaptive searching regions in aerial images. In Proc. of Springer ECCV. Springer, 651--664."},{"key":"e_1_2_1_71_1","volume-title":"Benchmarking tpu, gpu, and cpu platforms for deep learning. arXiv preprint arXiv:1907.10701","author":"Wang Yu Emma","year":"2019","unstructured":"Yu Emma Wang, Gu-Yeon Wei, and David Brooks. 2019. Benchmarking tpu, gpu, and cpu platforms for deep learning. arXiv preprint arXiv:1907.10701 (2019)."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00033"},{"key":"e_1_2_1_73_1","volume-title":"Radarnet: Exploiting radar for robust perception of dynamic objects. In Proc","author":"Yang Bin","year":"2020","unstructured":"Bin Yang, Runsheng Guo, Ming Liang, Sergio Casas, and Raquel Urtasun. 2020. Radarnet: Exploiting radar for robust perception of dynamic objects. In Proc. of Springer ECCV. Springer, 496--512."},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3380881"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341432"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684746.2689060"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00079"},{"key":"e_1_2_1_80_1","volume-title":"Proc. of USENIX Annual Technical Conference. 951--965","author":"Zhang Minjia","year":"2018","unstructured":"Minjia Zhang, Samyam Rajbhandari, Wenhan Wang, and Yuxiong He. 2018. Deepcpu: Serving rnn-based deep learning models 10x faster. In Proc. of USENIX Annual Technical Conference. 951--965."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448628"},{"key":"e_1_2_1_82_1","volume-title":"Autonomous Driving in Adverse Weather Conditions: A Survey. arXiv preprint arXiv:2112.08936","author":"Zhang Yuxiao","year":"2021","unstructured":"Yuxiao Zhang, Alexander Carballo, Hanting Yang, and Kazuya Takeda. 2021. Autonomous Driving in Adverse Weather Conditions: A Survey. arXiv preprint arXiv:2112.08936 (2021)."},{"key":"e_1_2_1_83_1","volume-title":"Vision meets drones: Past, present and future. arXiv preprint arXiv:2001.06303","author":"Zhu Pengfei","year":"2020","unstructured":"Pengfei Zhu, Longyin Wen, Dawei Du, Xiao Bian, Qinghua Hu, and Haibin Ling. 2020. Vision meets drones: Past, present and future. arXiv preprint arXiv:2001.06303 (2020)."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081339"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3550298","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3550298","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:40:02Z","timestamp":1752468002000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3550298"}},"subtitle":["Edge Assisted Real-time and Robust Object Detection on Drones via mmWave Radar and Camera Fusion"],"short-title":[],"issued":{"date-parts":[[2022,9,6]]},"references-count":85,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9,6]]}},"alternative-id":["10.1145\/3550298"],"URL":"https:\/\/doi.org\/10.1145\/3550298","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,6]]},"assertion":[{"value":"2022-09-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}