基于边缘增强与注意力机制的SAR舰船检测模型

SAR Ship Detection Model Based on Edge Enhancement and Attention Mechanism

合成孔径雷达图像用于舰船检测时,不可避免地受到相干斑噪声影响,并且近岸舰船检测易被复杂背景信号淹没。对此提出一种基于边缘特征融合网络的舰船检测算法RBox-YOLO,以YOLOv8为基线网络通过优化Canny算子边缘来增强图像中的边缘轮廓,形成较完整的物体边界。引入一种基于坐标注意力机制的FDN模块融合去噪后图像,以提高复杂背景下捕获关键信息的能力。采用双线性插值法的上采样与注意力机制结合的CAU模块,减少上采样带来的细节特征损失。另外,使用一种基于旋转框的损失函数来提高复杂背景下舰船的检测效果。实验结果表明,RBox-YOLO算法既保持了YOLOv8算法实时检测速度,平均精度还提高了8个百分点。初步判定RBox-YOLO算法具有良好的检测性能和较高的应用价值。

When using SAR images for ship detection,it is inevitable to be affected by speckle noise,and nearshore ship detection is easily overwhelmed by complex background signals.A ship detection algorithm RBox-YOLO based on edge feature fusion network is proposed.Using YOLOv8 as the baseline network,the edge of Canny operator is optimized to enhance the contour edges in the image,forming a more complete object boundary.An FDN module based upon coordinate attention mechanism is introduced to fuse denoised images to improve the ability of capturing key information in complex background.The CAU module,which combines bilinear interpolation method with attention mechanism,reduces the detail feature loss caused by upsampling.In addition,a loss function on the basis of rotating frame is used to enhance the ship detection effect under complex background.The experimental results show that RBox-YOLO not only maintains the real-time detection speed of YOLOv8 algorithm,but also improves the average accuracy by 8 percentage points.It is preliminarily concluded that RBox-YOLO algorithm has good detection performance and high application value.