Abstract
The unmanned aerial vehicles (UAVs) significantly contribute to the convenience and intelligence of life. However, the large use of UAVs also leads to high security risk. Only detecting the small and flying UAVs can prevent the safety accidents. UAV detection task could be regarded as a branch of object detection in flied of image processing. The advanced object detection models are mainly data driven, which depend on large-scale databases. The well-labeled datasets have proved to be of profound value for the effectiveness and accuracy in various object detection tasks. Thus, the first step of detecting UAVs is to build up a dataset of UAVs. In this study, we collect and release a dataset for UAV detection, called UAVData. To maintain the universality and robustness of the trained models, balloons and 6 types of UAVs are recorded in the dataset which totally consists of 13,803 well-labeled and recognizable images. We further conduct strong benchmarks using several advanced deep detection models, including faster R-CNN, SSD, YOLOv3. In addition, we utilize 4 different convolutional neural network models as the backbone models of these object detection methods to learn UAV-related features in images. By providing this dataset and baselines, we hope to gather researchers in both UAVs detection and machine learning field to advance toward the application.
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References
Abualigah LM (2019) Feature selection and enhanced krill herd algorithm for text document clustering, studies in computational intelligence, vol 816. Springer, Berlin
Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl 32(16):12381–12401
Abualigah L, Diabat A (2020) A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications. Neural Comput Appl 32(19):15533–15556
Abualigah LM, Shehab M, Alshinwan M, Alabool H (2020) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 32(15):11195–11215
Accetta JS, Shumaker DL (1993) The infrared & electro-optical systems handbook. SPIE Optical Engineering Press, Bellingham
Arqub OA, Abo-Hammour ZS (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415
Arqub OA, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput 24(16):12501–12522
Arqub OA, Al-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20(8):3283–3302
Arqub OA, Al-Smadi M, Momani S, Hayat T (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21(23):7191–7206
Bosquet B, Mucientes M, Brea V (2018) STDnet: a ConvNet for small target detection. In: British machine vision conference (BMVC)
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 248–255
Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88:303–338
Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision (ICCV), pp 1440–1448
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 580–587
Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37:583–596
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2261–2269
Jeon S, Shin JW, Lee YJ, Kim WH, Kwon Y, Yang HY (2017) Empirical study of drone sound detection in real-life environment with deep neural networks. In: European signal processing conference (EUSIPCO), pp 1858–1862
Kim BK, Kang HS, Park SO (2016) Drone classification using convolutional neural networks with merged doppler images. IEEE Geosci Remote Sens Lett 14:38–42
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097–1105
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324
Lin TY, Maire M, Belongie SJ, Bourdev LD, Girshick RB, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: European conference on computer vision (ECCV), pp 740–755
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision (ECCV), pp 21–37
Mezei J, Molnár A (2016) Drone sound detection by correlation. In: International symposium on applied computational intelligence and informatics (SACI)
Mohajerin N, Histon J, Dizaji R, Waslander SL (2014) Feature extraction and radar track classification for detecting UAVs in civillian airspace. In: Radar conference, pp 0674–0679
Park J, Park S, Kim DH, Park SO (2019) Leakage mitigation in heterodyne FMCW radar for small drone detection with stationary point concentration technique. IEEE Trans Microw Theory Tech 67:1221–1232
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788
Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 6517–6525
Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. https://arxivorg/abs/180402767
Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems (NIPS), pp 91–99
Rozantsev A, Lepetit V, Fua P (2017) Detecting flying objects using a single moving camera. IEEE Trans Pattern Anal Mach Intell 39:879–892
Ryu J, Kim S (2018) Small infrared target detection by data-driven proposal and deep learning-based classification. In: Infrared technology and applications XLIV, p 106241J
Schumann A, Sommer L, Klatte J, Schuchert T, Beyerer J (2017) Deep cross-domain flying object classification for robust UAV detection. In: IEEE international conference on advanced video and signal based surveillance (IEEE AVSS), pp 1–6
Shen Z, Liu Z, Li J, Jiang YG, Chen Y, Xue X (2017) Dsod: Learning deeply supervised object detectors from scratch. In: IEEE international conference on computer vision (ICCV), pp 1937–1945
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations (ICLR)
Sosnowski T, Bieszczad G, Madura H, Kastek M (2018) Thermovision system for flying objects detection. In: Baltic URSI symposium, pp 141–144
Torvik B, Olsen KE, Griffiths H (2016) Classification of birds and uavs based on radar polarimetry. IEEE Geosci Remote Sens Lett 13:1305–1309
Wargo C, Snipes C, Roy A, Kerczewski R (2016) UAS industry growth: forecasting impact on regional infrastructure, environment, and economy. In: Digital avionics systems conference (DASC), pp 1–5
Wu M, Xie W, Shi X, Shao P, Shi Z (2018) Real-time drone detection using deep learning approach. In: International conference on machine learning and intelligent communications (MLICOM), pp 22–32
Yang B, Li J, Wong DF, Chao LS, Wang X, Tu Z (2019) Context-aware self-attention networks. In: The thirty-third conference on artificial intelligence (AAAI). AAAI Press, pp 387–394
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Zeng, Y., Duan, Q., Chen, X. et al. UAVData: A dataset for unmanned aerial vehicle detection. Soft Comput 25, 5385–5393 (2021). https://doi.org/10.1007/s00500-020-05537-9
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DOI: https://doi.org/10.1007/s00500-020-05537-9