| Citation: | Jianwei LI, Changwen QU, Shujuan PENG, Yuan JIANG. Ship Detection in SAR images Based on Generative Adversarial Network and Online Hard Examples Mining[J]. Journal of Electronics & Information Technology, 2019, 41(1): 143-149. doi: 10.11999/JEIT180050
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Deep learning based ship detection method has a strict demand for the quantity and quality of the SAR image. It takes a lot of manpower and financial resources to collect the large volume of the image and make the corresponding label. In this paper, based on the existing SAR Ship Detection Dataset (SSDD), the problem of insufficient utilization of the dataset is solved. The algorithm is based on Generative Adversarial Network (GAN) and Online Hard Examples Mining (OHEM). The spatial transformation network is used to transform the feature map to generate the feature map of the ship samples with different sizes and rotation angles. This can improve the adaptability of the detector. OHEM is used to discover and make full use of the difficult sample in the process of backward propagation. The limit of positive and negative proportion of sample in the detection algorithm is removed, and the utilization ratio of the sample is improved. Experiments on the SSDD dataset prove that the above two improvements improve the performance of the detection algorithm by 1.3% and 1.0% respectively, and the combination of the two increases by 2.1%. The above two methods do not rely on the specific detection algorithm, only increase the time in training, and do not increase the amount of calculation in the test. It has very strong generality and practicability.
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