| Citation: | WANG Xikun, JIANG Hongxu, LIN Keyuet al. Remote sensing image ship detection based on modified YOLO algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1184-1191. doi: 10.13700/j.bh.1001-5965.2019.0394(in Chinese)
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Although the target detection algorithm has achieved very good detection results in data sets such as PASCAL VOC.However, the accuracy of ship target detection in large-scale prediction images is very low.Therefore, according to the characteristics of the visible light reflection image, a feature mapping module is added on the basis of the YOLOv3-Tiny algorithm, which provides rich semantic information for the prediction layer.At the same time, a residual network is used in the feature extraction network, which improves the detection accuracy and effectively extracts ship features. Experimental results show that the detection accuracy of the optimized M-YOLO algorithm is 94.12%.Compared with the SSD and YOLOv3 algorithms, the detection accuracy of the M-YOLO algorithm is improved by 11.11% and 9.44%.
| [1] |
KAZEMI F M, SAMADI S, POORREZA H R, et al.Vehicle recognition using curvelet transform and SVM[C]//4th International Conference on Information Technology.Piscataway: IEEE Press, 2007: 516-521.
|
| [2] |
DALAL N, TRIGGS B.Histograms of oriented gradients for human detection[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway: IEEE Press, 2005, 1: 886-893.
|
| [3] |
FREUND Y, SCHAPIRE R E.A desicion-theoretic generalization of on-line learning and an application to boosting[C]//European Conference on Computational Learning Theory.Berlin: Springer, 1995: 23-37.
|
| [4] |
BI F, ZHU B, GAO L, et al.A visual search inspired computational model for ship detection in optical satellite images[C]//IEEE Geoscience & Remote Sensing Letters.Piscataway: IEEE Press, 2012, 9: 749-754.
|
| [5] |
REN S, HE K, 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, 2017, 39(6):1137-1149. doi: 10.1109/TPAMI.2016.2577031
|
| [6] |
REDMON J, FARHADI A.YOLOv3: An incremental improvement[EB/OL].(2018-04-08)[2019-07-18].https://arxiv.org/abs/1804.02767.
|
| [7] |
LIU W, ANGUELOV D, ERHAN D, et al.SSD: Single shot MultiBox detector.ECCV 1[EB/OL].(2016-12-29)[2019-07-18].https://arxiv.org/abs/1512.02325.
|
| [8] |
LIN T Y, DOLLAA'R P, GIRSHICK R, et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway: IEEE Press, 2017: 2117-2125.
|
| [9] |
DAI J, LI Y, HE K, et al.R-FCN: Object detection via region-based fully convolutional networks[C]//Proceedings of the 30th International Conference on Neural Information Processing.La Jolla: NIPS, 2016: 379-387.
|
| [10] |
ZHANG R, YAO J, ZHANG K, et al.S-CNN ship detection from high-resolution remote sensing images[C]//ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016: 423-430.
|
| [11] |
KANG M, LENG X, LIN Z, et al.A modified faster R-CNN based on CFAR algorithm for SAR ship detection[C]//International Workshop on Remote Sensing with Intelligent Processing.Piscataway: IEEE Press, 2017: 1-4.
|
| [12] |
LIU Y, ZHANG M H, XU P, et al.SAR ship detection using sea-land segmentation-based convolutional neural network[C]//International Workshop on Remote Sensing with Intelligent Processing.Piscataway: IEEE Press, 2017: 1-4.
|
| [13] |
VAN ETTEN A.You only look twice: Rapid multi-scale object detection in satellite imagery[EB/OL].(2018-05-24)[2019-07-18].https://arxiv.org/abs/1805.09512.
|
| [14] |
REDMON J, FARHADI A.YOLO9000: Better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Visoin and Pattern Recognition(CVPR).Piscataway: IEEE Press, 2017: 6517-6525.
|
| [15] |
HE K, ZHANG X, REN S, et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway: IEEE Press, 2016: 770-778.
|
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