mmdetection-song gao.rar

As an outstanding method for ocean monitoring, Synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain few feature information. It is difficult to detect them from the background clutter, and there is a problem of low detection rate and high false alarm. To improve the detection accuracy for small-scale ships, we propose an efficient ship detection model based on YOLOX, called YOLO-SD. First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in overall performance.作为海洋监测的优秀技术手段，合成孔径雷达（Synthetic aperture radar，SAR）近年来受到学界的广泛关注。随着SAR技术与图像处理领域的快速发展，SAR图像船舶检测方向也取得了显著进展。针对广阔海面上的大型船舶，现有多数目标检测算法均可取得优异的检测效果。然而，SAR图像中的小型船舶所含特征信息极少，难以从复杂背景杂波中有效检出，存在检测率低、虚警率高的问题。为提升小型船舶的检测精度，我们提出了一种基于YOLOX的高效船舶检测模型，命名为YOLO-SD。首先，我们提出多尺度卷积（Multi-Scale Convolution，MSC）模块以融合多尺度特征信息，解决低层语义信息不平衡的问题，提升特征提取能力。进一步，我们设计了特征变换器模块（Feature Transformer Module，FTM），用于捕获全局特征并将其与上下文信息关联，以优化高层语义信息，最终实现优异的检测性能。在HRSID与LS-SSDD-v1.0数据集上开展的大量实验表明，YOLO-SD的检测性能优于基准模型YOLOX。相较于其他优秀的目标检测模型，YOLO-SD在整体性能上仍具备显著优势。