Laser Journal, Volume. 45, Issue 7, 111(2024)
A lightweight remote sensing image military aircraft detection model based on Faster R-CNN
[1] [1] Cheng G, Xie X, Han J. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3735-3756.
[3] [3] Xiao Y, Tian Z, Yu J. A review of object detection based on deep learning[J]. Multimedia Tools and Applications, 2020, 79: 23729-23791.
[4] [4] Zhao K, Ren X. Small aircraft detection in remote sensing images based on YOLOv3[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 533(1): 012-056.
[5] [5] Farhadi A, Redmon J. Yolov3: An incremental improvement[C]//Computer vision and pattern recognition. Berlin/Heidelberg, Germany, 2018, 1804: 1-6.
[7] [7] Shaoqing Ren, Kaiming He, Ross Girshick. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. Advances in Neural Information Processing Systems (NIPS). 2015: 1-8.
[8] [8] Liu L, Ouyang W, Wang X. Deep learning for generic object detection: A survey[J]. International journal of computer vision, 2020, 128: 261-318.
[9] [9] Zhang L, Li C, Zhao L. A cascaded three-look network for aircraft detection in SAR images[J]. Remote Sensing Letters, 2020, 11(1): 57-65.
[10] [10] Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT, USA, 2018: 6154-6162.
[12] [12] Liu W, Anguelov D, Erhan D. Ssd: Single shot multibox detector[C]//Computer Vision-ECCV 2016: 14th European Conference. Amsterdam, The Netherlands, 2016: 21-37.
[13] [13] Carion N, Massa F, Synnaeve G. End-to-end object detection with transformers[C]//European conference on computer vision. Online, 2020: 213-229.
[14] [14] Zhu X, Su W, Lu L. Deformable DETR: Deformable Transformers for End-to-End Object Detection[C]//International Conference on Learning Representations. Addis Ababa, Ethiopia, 2020: 1-16.
[15] [15] Zhang H, Li F, Liu S. Dino: Detr with improved denoising anchor boxes for end-to-end object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022. New Orleans, USA, 2022: 13619-13627.
[16] [16] Zhu X, Hu H, Lin S. Deformable convnets v2: More deformable, better results[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition2019. Long Beach, USA, 2019: 9308-9316.
[17] [17] Bodla N, Singh B, Chellappa R. Soft-NMS--improving object detection with one line of code[C]//Proceedings of the IEEE international conference on computer vision 2017. Hawaii, USA, 2017: 5561-5569.
[18] [18] Chen K, Wang J, Pang J. MMDetection: Openmmlab detection toolbox and benchmark[J]. arXiv preprint. 2019, 1906: 07155-07160.
[19] [19] Jian S, Kaiming H, Shaoqing R. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision & Pattern Recognition. 2016: 770-778.
[20] [20] Zhang H, Wu C, Zhang Z. ResNeSt: Split-Attention Networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022. New Orleans, USA, 2022: 2736-2746.
[21] [21] Dai J, Qi H, Xiong Y. Deformable convolutional networks[C]//Proceedings of the IEEE international conference on computer vision 2017. Venice, Italy, 2017: 764-773.
[22] [22] Neubeck A, Van Gool L. Efficient non-maximum suppression[C]//18th international conference on pattern recognition (ICPR'06). Hong Kong, China, 2006, 3: 850-855.
Get Citation
Copy Citation Text
DANG Yulong, YE Chengxu. A lightweight remote sensing image military aircraft detection model based on Faster R-CNN[J]. Laser Journal, 2024, 45(7): 111
Category:
Received: Jan. 12, 2024
Accepted: Dec. 20, 2024
Published Online: Dec. 20, 2024
The Author Email: Chengxu YE (149926237@qq.com)