Focusing on the airplanes in remote-sensing images, a real-time algorithm based on improved YOLOv3 is proposed to detect airplanes in remote-sensing images. Firstly, a convolutional neural network that consists of 49 convolutional layers is proposed to detect airplanes in remote-sensing images specifically. Secondly, dense connection is employed on proposed convolutional neural network, and maxpool is employed to enhance the feature transmit between dense blocks. Finally, to deal with the fact that airplanes in remote-sensing images are small targets mainly, we propose to increase the scale detection from 3 to 4 and employ dense connection to merge feature map among different scales. The algorithm is trained and tested on the designed airplane dataset. The experiment results show that our algorithm obtain 96.26% on precision and 93.81% on recall.
Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3
First published at:Dec 01, 2018
Opto-Electronic Engineering Vol. 45, Issue 12, pp. 180350 (2018)
1 Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 580-587.
2 Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision. IEEE, 2015: 1440-1448.
3 Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 91-99.
4 He K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]//IEEE International Conference on Computer Vision. IEEE, 2017: 2980-2988.
5 Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection[C]//Computer Vision and Pattern Recognition. IEEE, 2016: 779-788.
6 Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector[C]//European Conference on Computer Vision. Springer International Publishing, 2016: 21-37.
7 Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2017: 6517-6525.
8 Redmon J, Farhadi A. YOLOv3: An Incremental Improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
9 Xue Y J, Huang N, Tu S Q, et al. Immature mango detection based on improved YOLOv2[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(7): 173-179.
10 Wang S Y, Gao X, Sun H, et al. An aircraft detection method based on convolutional neural networks in high-resolution SAR images[J]. Journal of Radars, 2017, 6(2): 195-203.
11 Zhou M, Shi Z W, Ding H P. Aircraft classification in remote-sensing images using convolutional neural networks[J]. Journal of Image and Graphics, 2017, 22(5): 702-708.
12 Gu Y, Xu Y. Fast SAR target recognition based on random convolution features and ensemble extreme learning machines[J]. Opto-Electronic Engineering, 2018, 45(1): 170432.
13 Huang G, Liu Z, Maaten L V D, et al. Densely Connected Convolutional Networks[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2017: 2261-2269.
14 Lin T Y, Dollar P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2017: 936-944.
Supported by National High Technology Research and Development Program ("863"Program) of China (863-2-5-1-13B)
Get Citation: Dai Weicong, Jin Longxu, Li Guoning, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electronic Engineering, 2018, 45(12): 180350.