Method of Ship Target Oblique Frame Detection in Lightweight SAR Image Based on Recurrent Neural Network

Abstract

When ship targets appear in SAR images at different angles, their shapes and contours may change significantly. At present, target box detection algorithms often match and recognize based on templates with fixed shapes and directions. When the angle of ship targets changes, these templates may no longer be applicable, leading to the decline of detection algorithm performance, and it is difficult to accurately identify and locate targets. Therefore, for the purpose of solving the problem of angle sensitivity, the method of ship target oblique frame detection in lightweight SAR image based on recurrent neural network is studied to improve the effect of ship target oblique frame detection. Using recurrent neural network, the framework of ship target oblique frame detection in lightweight SAR images is established to ensure the detection accuracy, significantly reduce the demand for computing resources, and achieve more efficient detection. In this framework, SAR images are input in the input layer and transmitted to the hidden layer. The lightweight convolutional neural network is used as the hidden layer, and channel attention mechanism is introduced to improve the extraction effect of useful ship target features. The output layer processes the ship target characteristics, predicts the ship target center point heat map, and calculates the oblique frame vertex coordinates of the center point heat map, so as to have better adaptability to the ship targets that tilt or rotate in the SAR image, solve the angle sensitivity problem, and complete the ship target oblique frame detection. The volume Kalman filter algorithm is used to train the recurrent neural network, optimize the network weight, and improve the detection accuracy of ship target oblique frame. Experiments show that this method can effectively extract ship target features. Under different background, this method can accurately detect the slant frame of ship target. Under different occlusion rates, the robustness of the method is better.