## 1 Introduction  
Synthetic Aperture Radar (SAR) is a widely used sensor in industrial applications for object detection [1]. Compared to optical imagery, which can be affected by weather conditions, SAR utilizes radar imaging geometry, relying on electromagnetic echo reflection for imaging. As a result, SAR can be deployed on a broader range of platforms than optical sensors, offering a wider range of applications. Currently, SAR is employed on satellites, unmanned aerial vehicles (UAVs), airplanes for military reconnaissance, surface monitoring, marine surveillance, and many other industrial fields. However, SAR images are often sparse, irregular, and contain significant noise. Moreover, due to the large sizes of SAR images, targets occupy a small proportion of the image area, which complicates object detection tasks. With the advancements in artificial intelligence, Convolutional Neural Networks (CNNs) have become widely adopted for image segmentation and object detection tasks [2]. This is particularly true in the field of SAR image interpretation, where deep learning algorithms are essential for processing large volumes of data [3]. Currently, there are two main branches of object detection methods for SAR images based on CNNs: two-stage algorithms, such as regions with Convolutional Neural Network features (R-CNN) [4], and single-stage algorithms, such as You Only Look Once (YOLO) [5] and Single Shot Multibox Detector (SSD) [6]. R-CNN employs a two-step process for target detection: first, it generates regional proposals, and then it predicts the positions of targets within those proposed regions. While R-CNN and similar methods are accurate, they are computationally slow due to the double-counting issue, making them unsuitable for real-time processing. In contrast, single-stage object detection algorithms use a regression strategy to directly extract features from the original image, making them faster than R-CNN and capable of achieving frame rates of up to 100 frames per second (FPS). Although the precision of the initial YOLO versions was lower than that of R-CNN, YOLOv5 [7] significantly improved both precision and speed, surpassing R-CNN in performance. Furthermore, YOLOv8 [8], an enhanced version of YOLOv5, has become the state-of-the-art (SOTA) model in object detection, offering even stronger performance. In response to the challenge of detecting objects in complex backgrounds within SAR images, we selected the open SAR dataset MSAR-1.0 [9] for our study and developed an improved YOLOv8 algorithm called MDD-YOLOv8. This model is tailored to the characteristics of SAR images, offering improved accuracy and fewer detection errors. Experimental results show that MDD-YOLOv8 significantly outperforms YOLOv8 in terms of precision, recall, and mAP. Additionally, the results of generalization experiments demonstrate that MDD-YOLOv8 is robust across other SAR datasets. The main contributions of our method are as follows:  
* To effectively detect tiny objects in SAR images, we modified the structure of the FPN-PAN [10] network by adding a new detection header specifically for small targets. The M-FPN-PAN network significantly improves recall rates by focusing on pixel-level details that may be overlooked by the original model [11]. * To make the structure of M-FPN-PAN more flexible and compatible with the YOLO architecture, we incorporated dynamic convolution [12] into the backbone. The C2f_DynamicConv module enhances precision while replacing some convolution operations with linear operations, thereby reducing computational complexity despite having more parameters. * To expand the receptive field of the backbone, we replaced the C2f module with the C2f_DLKA module. Additionally, with the assistance of the deformable large kernel attention mechanism [13], the precision increased to 87.7%, which is 8.1% higher than YOLOv8. In summary, MDD-YOLOv8 excels at object detection tasks and is highly suitable for industrial applications.