## 2 Related Work
### Deep Learning Approaches in Remote Sensing Applications
The proposed approach achieved good performance for ship detection with a classification accuracy of up to 83.8%. Similarly, [PERSON] et al. [42] proposed an improved version of the OSIRIS system, devoted to understanding maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. In the intelligent modeling process, the PRB-FPN method is designed to accurately identify various ship objects by integrating features at multiple scales, significantly improving the accuracy and stability of small-scale ship recognition. In addition, some researchers are beginning to experiment with different approaches to modeling. [PERSON] et al. [43] employed hollow residual convolution technology to extract shallow features and integrated these with deep information, resulting in a significant enhancement in the accuracy of aircraft detection within remote sensing images. [PERSON] et al. [44] introduced a multi-stage cascaded algorithm based on the Swin Transformer for object detection in remote sensing imagery, which has advanced the precision of horizontal frame marking tasks. These methodologies enhance target detection accuracy by broadening the receptive field and merging shallow and deep features for multi-scale feature fusion, though this may come at the expense of real-time performance. However, in the domains of agricultural and food logistics, there remains a critical need for further research to adapt existing techniques and address the unique challenges associated with remote sensing image detection. Traditional horizontal bounding boxes for detection often overlook the directional information of various logistics vehicles. To mitigate this issue, the RRPN (Rotation Region Proposal Network) introduces a Rotation RoI Pooling (RRol Pooling) layer, which refines the conventional horizontal bounding boxes into rotation-aware detection boxes. Despite the algorithm's high accuracy, the use of an excessive number of anchor boxes can result in substantial computational overhead, adversely affecting the model's processing speed. There is significant potential for enhancing detection performance, particularly concerning multi-scale targets within complex background remote sensing images, with a special focus on small-scale targets. The primary challenge is effectively extracting semantic information from small targets while minimizing false positives and missed detections. Current object detection algorithms exhibit considerable potential for advancement in remote sensing image detection. Remote sensing images captured from aerial perspectives often display object contours from a top-down view, which markedly differs from typical object features. The elevated shooting angle causes targets to appear small and densely packed in the images, which can lead to substantial feature information loss during extraction and may complicate model detection. Moreover, remote sensing images frequently feature multiple target types with varying aspect ratios, distinct orientations, and dense distributions. This results in rich background information within horizontal boundary areas, significant overlap and intersection among bounding boxes of different targets, and the presence of multiple object categories within a single bounding box. These factors considerably disrupt the model's training process, leading to pronounced issues such as false positives, missed detections, and difficulties in accurately distinguishing between different targets. To address these challenges, our research explores the development of a rotating object detection algorithm, EMRY-Net, tailored for food logistics and transportation, which aims to optimize and enhance the resolution of these issues.