Large Convolution Kernel Network With Edge Self-Attention for Oriented SAR Ship Detection

Synthetic aperture radar (SAR) ship detection is a popular area in remote sensing, which has broad applications in fishery management, maritime rescue and marine detection. In recent years, deep learning has been successfully applied in this field, but SAR ship detection still faces some challenges, such as speckle noise, strong scattering interference from complex backgrounds, and insufficient use of SAR image characteristics. These issues result in inaccurate ship positioning in complex scenes and a high rate of false alarms. To address the above challenges, we propose a large convolution kernel network with edge self-attention for oriented SAR ship detection (LKE-Det), which can accurately detect arbitrarily rotated ship targets in SAR images. First, we propose a backbone network based on large convolutional kernels, which has a large receptive field and can enhance the long-range dependencies of ships. The backbone adopts a bifurcated gate unit structure to combine the multiscale parallel convolution kernel module and the large convolution kernel attention module. The structure captures feature information of different scales while obtaining sufficient context information. Second, we adopt an edge self-attention mechanism, which uses an edge detection algorithm to extract the gradient matrix of the image and integrate it into the feature pyramid structure to effectively enhance the contour information of ships. The extensive experiments on SSDD and RSDD-SAR datasets demonstrate that our LKE-Det outperforms the state-of-the-art methods.