## 1 Introduction
Consequently, the process of extracting feature information for such minute targets becomes prone to loss during the model's feature extraction phase, further exacerbating the detection challenges. Taking the cargo ship as an example, as one of the most important transport carriers in agricultural product supply, the number of pixels it occupies in large-scale remote sensing images is often very small [16]. Moreover, many ship targets often present a dense aggregation distribution with obvious directionality, which means there is a lot of background information in the real area labeled by the horizontal box. There is a severe overlap and intersection phenomenon in different targets. Even the labeled boxes of a ship target contain more than one object, which brings a great deal of interference in the training process of the model, leading to a more serious misdetection and omission phenomenon in the model training process [17]. These aforementioned problems are mixed together, so existing deep learning methods cannot accurately identify the transport carrier targets in remote sensing images, such as cargo ships, trucks, aircraft, etc. Thus, in the actual application of agricultural product supply, determining how to build a better intelligent method to directly process remote sensing data is a significant issue that needs to be solved urgently. This paper addresses the challenges associated with target detection in remote sensing images by proposing a novel rotating target detector named EYMR-Net, which builds upon the single-stage YOLOv7 detection framework. The primary contributions of this study encompass the following two aspects:  
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2. To address the leakage detection issue of each small-scale target with dense distribution in remote sensing images, this study designed a feature extraction module on the backbone network of YOLOv7 by incorporating the Swin Transformer. The network's perceptual ability is enhanced for multi-scale targets, enabling the extraction of more comprehensive feature information. Additionally, the CBAM (Convolutional Block Attention Module) is integrated into the neck module to augment the model's capacity to analyze multi-scale targets, particularly capturing fine-grained details of dense small targets. These network enhancements contribute to improving the detection performance for dense small targets, which successfully suppresses the error of omissions and failures in detecting transport equipment from remote sensing images. 2. To improve the position and orientation information of various objects, this study introduces rotating box annotations to the horizontal regression model of the YOLO framework. This modification effectively addresses two main issues. On one hand, it resolves the problem of ignoring the directionality of the YOLO algorithm when detecting targets such as ships and vehicles, ensuring accurate identification. On the other hand, it mitigates the issue of excessive redundant pixels in the detection box caused by objects with large aspect ratios detected using the horizontal box approach. By incorporating rotating box annotations, the proposed model can efficiently meet the demands of real-time monitoring applications of remote sensing in the field of agricultural supply. This study is structured as follows: Section 2 introduces the detection solutions in related works and their shortcomings. Then, we introduce the proposed EYMR-Net in Section 3 and describe its principle and implementation process. Subsequently, experiments are conducted to validate the superiority of EYMR-Net, providing further evidence of the feasibility and effectiveness of the proposed method in this study. Finally, this study summarizes its contributions and outlines directions for future research.