## 1 Introduction
Thus, determining how to accurately distinguish the category, quantity, location, and other information of various transportation vehicles is vital for improving the supply ability in international trade. In addition, remote sensing images have long been plagued by some issues, such as large-scale ranging, low pixel quality, mutual occlusion, and a dense distribution of individual objects, making accurately identifying objects in remote sensing images a challenge; therefore, this topic has significant value and is worth studying to guarantee the efficiency of agricultural product circulation. From a specific technical point of view, remote sensing target detection is a crucial aspect of computer vision research. It involves identifying and localizing specific targets in remote sensing images by determining their category and rectangular enclosing box coordinates. The detection process typically consists of three steps: selecting the region of interest, extracting features from the selected region, and detecting and classifying the extracted features. Remote sensing images differ from natural images in several ways. They are primarily captured from altitudes ranging from several thousand meters to tens of thousands of meters, resulting in significant variations in target scales and perspectives. Furthermore, remote sensing images are captured under different weather conditions and angles, leading to diverse factors such as lighting changes, seasonal variations, and varying imaging quality. As a result, target detection based on remote sensing images presents challenges that include complex background issues, dense small target problems, and multi-directional complexities [11; 12]. These challenges necessitate innovative approaches to address them effectively. Traditional methods for target detection in remote sensing images can be categorized into four types: those based on statistical features of gray-scale information, visual saliency,template matching, and classification learning [13]. While these methods are computationally efficient, they often focus on specific scenes. In contrast, remote sensing photographs have a wide field of view, and high-resolution remote sensing photographs capture detailed surface features from multiple scenes. Traditional remote sensing image detection methods are insufficient for a fine-grained analysis of complex remote sensing scenes and lack robustness. Therefore, their applicability in target detection for high-resolution remote sensing photographs is limited [14]. With the rapid advancement of deep learning technology, a large number of studies also have gradually been using deep learning networks to realize target detection in remote sensing images to complete different applications of practical scenarios. Those approaches enable the effective integration of multi-scale features extracted from different dimensionalities to improve detection accuracy. Moreover, they can incorporate various learning strategies and skills, including attention mechanisms, the weighted summation strategy, dynamic parameter fine-tuning, etc., to further improve the identification ability and localization accuracy of each small object in remote sensing images [15]. Therefore, some deep neural network models designed for target detection in general image processing, such as the YOLO-serial and Faster-RCNN models, also migrate well to remote sensing applications. However, the current state of deep learning-based algorithms reveals significant potential for advancements, specifically in detecting small targets within remote sensing images. Aerially captured imagery often portrays objects from an overhead shooting angle, highlighting unique top contour features that differentiate them from common objects. However, the high perspective of remote sensing images presents a notable hurdle, that is, different targets within these images tend to be small and densely arranged.