## 3 Proposed Methods  
### The Structure of YOLOv8 Network  
The YOLO algorithm series is renowned for its speed and accuracy in performing object detection and image segmentation tasks [27; 28]. As a result, it has been widely adopted in fields such as traffic monitoring, rescue operations, military reconnaissance, and various industrial applications [29]. Notably, YOLOv5 has achieved great success when implemented in UAVs. YOLOv8, an improved version of YOLOv5 developed by Ultralytics, addresses the weaknesses of its predecessor, delivering enhanced performance. The structure of YOLOv8 is illustrated in Figure 1. One of its key innovations is the transition from the anchor-based mechanism of YOLOv5 to an anchor-free approach, reducing the reliance on anchor boxes and making the model easier to train. YOLOv8 consists of three main components: the backbone, neck, and head. The backbone of YOLOv8 is based on Darknet-53 with 10 layers. It replaces the C3 module (used in YOLOv5) with C2f, a simplified and computationally efficient alternative. At the first layer, YOLOv8 uses the CBS module to replace YOLOv5's Focus module. In the final layer of the backbone, YOLOv8 employs Spatial Pyramid Pooling Fusion (SPPF) to extract features and normalize multi-scale feature vector dimensions. The neck network in YOLOv8 retains the FPN-PAN architecture from YOLOv5 but introduces some simplifications, such as replacing C3 with C2f and removing two convolutional layers. The head of YOLOv8 features three detection headers, each corresponding to different target sizes: small, medium, and large. YOLOv8 also refines the loss function calculation by dividing it into two branches: a classification branch using BCE Loss and a regression branch employing CIoU and DFL. Despite these advancements, YOLOv8 is not universally adaptive to all imaging modalities. In particular, in SAR images, targets tend to be tiny, irregular, and prone to misdetection or inaccurate detection. As such, the YOLOv8 architecture requires further adjustments to effectively perform object detection in SAR images. As the analysis above suggests, targets in SAR images are often difficult to identify due to their indistinct boundaries. In general, SAR images are quite large, with targets occupying only a small portion of the image. When performing object detection in SAR images, a common challenge is that targets may blend into the background, making it difficult to improve the recall rate and leading to a high probability of missed or incorrect detections. To address this issue, we have enhanced the YOLOv8 architecture, resulting in MDD-YOLOv8 (shown in Figure 2), which is more adaptable to SAR images compared to the base model. We made several modifications to the YOLOv8 network structure in three key steps:  
Step 1: Given the small size of the targets, we augmented the feature fusion network to better capture the fine details in SAR images. Additionally, we added a connection from the backbone to the neck, which required the introduction of an additional detection head in the head section. These changes form a new network structure called modified FPN-PAN (M-FPN-PAN). Step 2: The static convolutions in the CSPLayer with two convolution (C2f) modules of YOLOv8 were not flexible enough to adapt to the architectural transformations required for SAR image detection. To improve this, we replaced the static convolutions with dynamic convolutions. As shown in Figure 2, we substituted C2f with C2f_DynamicConv in layers 2  
Figure 1: The structure of the YOLOv8 network. and 4. Section 4 will demonstrate that while using C2f_DynamicConv alone yields marginal improvements, combining it with M-FPN-PAN leads to significant performance gains. Step 3: To address the limited receptive field of the original model, we introduced an attention mechanism known as deformable large kernel attention (DLKA) at the bottom of the backbone in layer 8. This adjustment allows the network to better handle SAR images, which often require a broader receptive field for effective detection.