Abstract
Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. Moreover, most of them ignore the effective retention of position information in the feature extraction process, which reduces the contribution of features to subsequent classification. To overcome these limitations, we propose a novel ship detection framework combining the dilated rate selection and attention-guided feature representation strategies, which can efficiently detect ships of different scales under the interference of complex environments such as clouds, sea clutter and mist. Specifically, we present a dilated convolution parameter search strategy to adaptively select the dilated rate for the multi-branch extraction architecture, adaptively obtaining context information of different receptive fields without sacrificing the image resolution. Moreover, to enhance the spatial position information of the feature maps, we calculate the correlation of spatial points from the vertical and horizontal directions and embed it into the channel compression coding process, thus generating the multi-dimensional feature descriptors which are sensitive to direction and position characteristics of ships. Experimental results on the Airbus dataset demonstrate that the proposed method achieves state-of-the-art performance compared with other detection models.
Highlights
Ship detection is of great significance to maritime transportation, port management, disaster rescue and other activities
With the development of deep network, the learning-based method based on a convolutional neural network (CNN) has become the main idea in the field of ship detection [4]
Different from other methods, we propose an automatic dilated rate selection strategy in a dilated convolution kernel based on training data, which can obtain optimized effective receptive fields for ships of different scales
Summary
Ship detection is of great significance to maritime transportation, port management, disaster rescue and other activities. It is necessary to study how to make full use of the characteristics of different dimensions, providing more effective information for subsequent object classification To solve these problems mentioned above, we propose a novel ship detection method combining the dilated rate selection and attention-guided feature representation strategies. We employ a dilated convolution parameter search strategy to adaptively select the dilated rate, obtaining the multi-scale and multi-receptive-field characteristic information of the ship target. On this basis, to enhance the spatial position information of the feature maps, we calculate the correlation between two orthogonal directions in the spatial domain and embed it into the channel information coding, generating the multidimensional feature descriptors which are sensitive to direction and position characteristics of ships.
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