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Multi-Size Convolution and Learning Deep Network for SAR Ship Detection From Scratch
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DOI：10.1109/ACCESS.2020.3020363.png)
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Abstract
En 中文
Synthetic aperture radar (SAR) ship detection is a popular branch of SAR interpretation. A growing number of scholars are devoting themselves to applying convolutional neural network (CNN) to SAR ship detection. Currently, most CNN-based SAR ship detectors are variants of object detectors in optical images; however, the essential differences between SAR and optical images restrict their performance. To this end, by focusing on the attribute of SAR image's point property which is determined by its imaging mechanism, we design a novel SAR ship detector from scratch. The innovatively designed PCB-MSK (parallel convolutional block of multi-size kernels) consists of two groups of convolutions, each group is composed of four convolutional layers corresponding to kernel sizes of 3, 5, 7, and 9; the stride is 1 for one group and 2 for another. In the designed convolutional module with features reused (CMFR), the output and input feature maps of the previous block are concatenated for current layer to reduce information loss during forward propagation and to strengthen the supervision for shallow layers during parameter optimization. For each source prediction layer, the binary classification is first conducted to alleviate the positive/negative imbalance; deconvolution and feature fusion are utilized to enhance the feature representation. Then, we perform fine detection. Experiments on RDISD-SAR and SSDD, in which RDISD-SAR is meticulously constructed by us based on two open-access datasets, show that our method achieves a state-of-the-art accuracy and competitive speed, the average precision (AP) reaches 88.70% and 90.57% for RDISD-SAR and SSDD, respectively. These APs are 10.43% and 4.23% higher than DSOD, and 6.64% and 1.70% higher than ScratchDet. The detection speed is 58.2 FPS on a GTX 1080Ti GPU, the number of parameters is 18.19M and the amount of computations is 21.33G. In addition, experimental results show that the robustness of our detector is very excellent.
Keywords:
Marine vehicles
Synthetic aperture radar
Detectors
Feature extraction
Convolution
Optical detectors
Optical imaging
Ship detection
CNN
SAR dataset
scratch
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