SAR ship detection algorithm based on global position information and fusion of residual feature

In view of the problem that ship targets in synthetic aperture radar (SAR) images have varying scales and are easily affected by sea clutter, ground clutter, and coherent speckle noise, which makes it difficult to extract target multidimensional features and leads to semantic ambiguity during feature fusion, resulting in low ship target detection rale and high false alarm rale, a SAR ship largel detection algorithm based on global position information and residual feature fusion is proposed. Based on the Faster region convolutional neural network (R-CNN) object detection algorithm, improvements are made in the feature extraction network and feature fusion network. A height width alienlion mechanism is used in the feature extraction network to extract the global position information of the largel in the image, enhancing lhe multidimensional feature extraction capability of the largel. A bidirectional feature pyramid network with residual connections is used in the feature fusion network to reduce semantic ambiguity in the feature fusion process, reduce false alarms of ship targets in complex backgrounds, and perform bidirectional fusion of multi-scale features at different levels to enhance the connection between high and low-level features and improve the detection capability of multi-scale ship targets. The algorithm achieves a mean average precision of 98.2% on the SAR ship dataset, surpassing some algorithms by 2.4% or more. The experiments show that the proposed algorithm effectively extracts multidimensional features of the target, significantly alleviates semantic ambiguity problems, and has good detection and generalization capabilities. © 2024 Chinese Institute of Electronics. All rights reserved.