An improved anchor-free model based on attention mechanism for ship detection
In order to improve the detection capability of detectors for multiscale ships in SAR images and ensure the real-time performance of the detection networks,an improved anchor-free model based on attention mechanism for ship detection is proposed.On the basic framework of the off-the-shelf YOLOX,a lightweight dilated convolutional attention module(DCAM)is embedded in front of feature pyramid network(FPN)to adjust the relationship between receptive field and multiscale fusion,and strengthen the representation ability of features.The detection head is redesigned by introducing the center-ness prediction branch,which can weight the classification scores of the anchor points,in the meantime,the loss function of the proposed model is also revised to optimize the final detection performance.Through the comparative experiments on dataset SSDD,the proposed model in this paper is superior to the mainstream deep learning detection models,with an accuracy of 94.73%,and achieves the best trade-off between detection accuracy and detection speed.
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