Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0628009(2023)
Object Detection Method Based on Improved YOLOv4 Network for Remote Sensing Images
[1] Ševo I, Avramović A. Convolutional neural network based automatic object detection on aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 13, 740-744(2016).
[2] Wang P, Liu R, Xin X J et al. Scene classification of optical remote sensing images based on residual networks[J]. Laser & Optoelectronics Progress, 58, 0210001(2021).
[3] Wang Y Q, Ma L, Tian Y. State-of-the-art of ship detection and recognition in optical remotely sensed imagery[J]. Acta Automatica Sinica, 37, 1029-1039(2011).
[4] Wang Y N, Wang X L. Remote sensing image target detection model based on attention and feature fusion[J]. Laser & Optoelectronics Progress, 58, 0228003(2021).
[5] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).
[6] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).
[7] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).
[8] Redmon J, Farhadi A. Yolov3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767
[10] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).
[11] He K M, Gkioxari G, Dollar P et al. Mask R-CNN[C], 386-397(2017).
[12] Tian Z, Shen C H, Chen H et al. FCOS: fully convolutional one-stage object detection[C], 9626-9635(2019).
[13] Duan K W, Bai S, Xie L X et al. CenterNet: keypoint triplets for object detection[C], 6568-6577(2019).
[14] Zhang W H, Jiao L C, Liu X et al. Multi-scale feature fusion network for object detection in VHR optical remote sensing images[C], 330-333(2019).
[15] Han S, Pool J, Tran J et al. Learning both weights and connections for efficient neural networks[C], 1135-1143(2015).
[18] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11218, 122-138(2018).
[19] Yang J C, Zhu Y H, Jiang B et al. Aircraft detection in remote sensing images based on a deep residual network and Super-Vector coding[J]. Remote Sensing Letters, 9, 228-236(2018).
[20] Xu Y L, Zhu M M, Xin P et al. Rapid airplane detection in remote sensing images based on multilayer feature fusion in fully convolutional neural networks[J]. Sensors, 18, 2335(2018).
[21] Liu M J, Wang X H, Zhou A J et al. UAV-YOLO: small object detection on unmanned aerial vehicle perspective[J]. Sensors, 20, 2238(2020).
[22] Xu D Q, Wu Y Q. Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection[J]. Sensors, 20, 4276(2020).
Get Citation
Copy Citation Text
Zhenjiu Xiao, Yueying Yang, Xiangxu Kong. Object Detection Method Based on Improved YOLOv4 Network for Remote Sensing Images[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0628009
Category: Remote Sensing and Sensors
Received: Dec. 30, 2021
Accepted: Jan. 21, 2022
Published Online: Mar. 16, 2023
The Author Email: Yueying Yang (719633801@qq.com)