Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1215017(2022)
Detection and Tracking of Low-Altitude Unmanned Aerial Vehicles Based on Optimized YOLOv4 Algorithm
[1] Bai L, Lu Z, Du X R et al. Rules and methods of UAV activities’aerial lanes design for(ultra) low airspace in regional areas[J]. Advances in Earth Science, 31, 1197-1204(2016).
[6] Wang W F, Jin J, Chen J M. Rapid detection algorithm for small objects based on receptive field block[J]. Laser & Optoelectronics Progress, 57, 021501(2020).
[9] Dai J F, Li Y, He K M et al. R-FCN: object detection via region-based fully convolutional networks[C], 379-387(2016).
[13] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. http://arxiv.org/abs/1804.02767
[15] Ma Q, Zhu B, Cheng Z D et al. Detection and recognition method of fast low-altitude unmanned aerial vehicle based on dual channel[J]. Acta Optica Sinica, 39, 1210002(2019).
[16] Ju M R, Luo H B, Wang Z B et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Acta Optica Sinica, 39, 0715004(2019).
[17] Sun Y C, Pan S G, Zhao T et al. Traffic light detection based on optimized YOLOv3 algorithm[J]. Acta Optica Sinica, 40, 1215001(2020).
[25] Xu K, Deng C. Research on helmet wear identification based on improved YOLOv3[J]. Laser & Optoelectronics Progress, 58, 0615002(2021).
[26] Liu F, Guo M, Wang X J. Small target detection based on cross-scale fusion convolution neural network[J]. Laser & Optoelectronics Progress, 58, 0610012(2021).
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Yuemeng Zhao, Huigang Liu. Detection and Tracking of Low-Altitude Unmanned Aerial Vehicles Based on Optimized YOLOv4 Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215017
Category: Machine Vision
Received: Aug. 2, 2021
Accepted: Sep. 8, 2021
Published Online: May. 23, 2022
The Author Email: Liu Huigang (liuhg@nankai.edu.cn)