A Method for Nearshore Vessel Target Detection in SAR Imagery Utilizing Edge Characteristics and Augmented Global Information Amplification

Abstract

Synthetic aperture radar (SAR), which can work normally under various meteorological conditions, has been widely researched and applied in marine vessel target monitoring and identification. Among the many research topics, due to the inconsistency of ship scale in SAR images, susceptibility to sea and land noise and clutter interference, resulting in a low detection rate of near-shore ship targets and inaccurate edge delineation of densely lined ships, a target detection algorithm based on Deep Convolutional Neural Network (DCNN) is proposed. The algorithm employs the Channel-Space Grouping attention mechanism (CSG) during feature extraction to enhance features by utilizing global positional and edge information associated with instances. The Feature Mobility Fusion module (FMF) is employed to merge features of various scales, bolster the interconnection among these features, and enhance multi-scale ship target detection capabilities. The decoupled head is employed for ship target localization, while the angle-Weighted Intersection over Union (angle-WIoU) is used to mitigate regression errors. The experimental results show that the precision (P) achieved on HRSID and SSDD datasets reaches 94.81% and 99.01% respectively, exceeding the control algorithm by more than 1.35% and 0.94%; the average precision (mAP) reaches 92.06% and 99.50% respectively, exceeding the control algorithm by more than 2.32% and 2.51%; this indicates that the proposed algorithm has a good performance on SAR image ship detection and a strong generalization ability.