Electronics Optics & Control, Volume. 29, Issue 12, 101(2022)
Detection of Aircrafts in Remote Sensing Images Based on Improved YOLOv4
[1] [1] YI D W,SU J Y,CHEN W H.Probabilistic faster R-CNN with stochastic region proposing:towards object detection and recognition in remote sensing imagery[J].Neurocomputing,2021,459:290-301.
[2] [2] VHARKATE M N,MUSANDE V B.Remote sensing image retrieval using hybrid visual geometry group network with relevance feedback[J].International Journal of Remote Sensing,2021,42(14):5540-5567.
[4] [4] WU J,CAO C Q,ZHOU Y D,et al.Multiple ship tracking in remote sensing images using deep learning[J].Remote Sensing,2021,13(18):3601.
[7] [7] DAOUDI S,ZOUAOUI C M A,EL-MEZOUAR M C,et al.Parallelization of the K-means++ clustering algorithm[J].Ingénierie des Systèmes D Information,2021,26(1):59-66.
[8] [8] HOSSAIN M S,BETTS J M,PAPLINSKI A P.Dual Focal Loss to address class imbalance in semantic segmentation[J].Neurocomputing,2021,462:69-87.
[9] [9] WANG J H,LI G Y,ZHANG W Z.Combine-Net:an improved filter pruning algorithm[J].Information,2021,12(7):264.
[10] [10] CHEN B L,WAN J J,CHEN T Y,et al.A self-attention based faster R-CNN for polyp detection from colonoscopy images[J].Biomedical Signal Processing and Control, 2021,70:103019.
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ZHANG Tianjuna, LIU Yuhuaib, LI Suchenc. Detection of Aircrafts in Remote Sensing Images Based on Improved YOLOv4[J]. Electronics Optics & Control, 2022, 29(12): 101
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Received: Oct. 25, 2021
Accepted: --
Published Online: Feb. 4, 2023
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