## 2 Related Work
### Deep Learning Approaches in Remote Sensing Applications  
Over the past few years, the detection application of remote sensing images has held immense value in the agricultural supply chain, accurately identifying various vehicle types and transportation directions. We can obtain large-scale ground transportation information by analyzing and processing remote sensing images, providing strong support for food logistics management and transportation planning. The development and progress of image detection technology have brought many innovations and challenges to food transportation. On the one hand, by utilizing the feature information in remote sensing images, we can effectively detect different types of vehicles, such as trucks, ships, planes, etc., thereby achieving accurate transportation frequency and position. On the other hand, remote sensing image object detection technology also provides a convenient and efficient means for food traffic monitoring and management, which can better grasp the logistics transportation situation and improve logistics efficiency and safety. However, remote sensing image object detection still faces certain challenges and issues, including the low accuracy of object detection at various scales, the missed detection of dense small targets in complex backgrounds, and limitations of horizontal detection boxes. In response to the problem of low accuracy in object detection across different scales, [PERSON] et al. [34] improved the problem of insufficient information and small object detection by adding a Transformer structure to the two-level network. Similarly, [PERSON] et al. [35] improved the detection ability of small targets by adding ECA attention to shallow convolutional layers. Although this algorithm effectively detects small targets, it still demonstrates inadequate detection results for object detection at various scales. It struggles to adapt to the diverse scale variations present in remote sensing image targets. Therefore, to boost object detection performance across different scales, [PERSON] et al. [36] combined the YOLOv7 algorithm with the gap space pyramid and attention to construct a feature fusion module, which improved the feature extraction capability for multi-scale objects. In order to address the issue of poor detection performance for densely packed small targets, such as ships, in complex backgrounds of remote sensing images, [PERSON] et al. [37] designed an Enhanced Feature Pyramid Network (EFPN), which utilizes scale-sensitive relationships and adaptive balanced feature integration (ABFI) modules to improve the ability to recognize different sizes of ships. [PERSON] et al. [38] used the repeated bilateral feature pyramid network (DB-FPN), which has shown promise in enhancing the recognition of dense targets in complex backgrounds, such as ships. Similarly, [PERSON] et al. [39] embedded the coordinate attention mechanism into the spatial pyramid structure to construct the optimized YOLOv7-Tiny model and optimized it by using the SIoU loss function. It made progress in model lightweighting and detection accuracy and could realize the accurate identification of transport vehicles such as ships. Recently, more and more researchers have begun to study the problem of missing detection and inaccurate orientation in remote sensing images. For example, [PERSON] et al. [40] proposed a modeling method based on multi-modal remote sensing data fusion to monitor ship types and statuses, which contribute to the establishment of a novel maritime surveillance system. Different from other existing approaches focusing on the extraction of ship locations using either hull or wake information, [PERSON] et al. [41] proposed a detection method on the basis of a cascaded structure to locate the ship category using high-resolution satellite imagery. Through the phase spectrum of Fourier transform, ship classification was conducted using a fuzzy classifier combining both hull and wake information.