## 1 Introduction  
The acquisition of yield information for citrus and other cash crops is an important part of the fine technology system and a key indicator for orchard production management by fruit farmers [1]. It is also an important study direction for field information management in smart orchards. Citrus is one of the most widely planted agricultural products in the world, which is an important pillar of the agricultural economy in many countries, and it is also an important cash fruit crop in Southwest China [2]. With the continuous development of citrus planting technology, efficient cultivation patterns such as dense planting, fine pruning, and citrus bagging have become mainstream [3], which has greatly improved the efficiency of orchard land use. The accurate prediction of citrus production can help to effectively utilize resources and rationally allocate resources such as labor, storage space, and harvesting equipment [4]. However, at present, the number of fruits in intensively cultivated citrus orchards mainly relies on manual sampling and counting estimation,which is time-consuming and laborious. Therefore, the automatic counting of citrus fruits is essential for obtaining yield information in citrus orchards. In recent years, automatic counting methods oriented to fruit yield measurement have become the focus of study, but their studies have mainly focused on static models. [PERSON] et al. [4] used the Viola-Jones based target detection algorithm to estimate the yield by counting kiwifruit images in the field and tested in two large kiwifruit orchards, which resulted in counting errors of 6% and 15%, respectively. [PERSON] et al. [5] proposed a deep learning model called \"MangoYOLO\", trained on daytime images and robust to variations in orchards, varieties, and lighting conditions. It estimated fruit yields in five orchards with errors ranging from 4.6% to 15.2%. Although these related studies achieved good counting performance on static images, the static model is not applicable to the actual production environment, so the detection and tracking of the dynamic model have become a hot spot for future research. Fast and effective fruit detection in dynamic modeling is the basis for achieving high-precision counting. [PERSON] et al. [6] established a color model for citrus recognition on R-B color metrics using the dynamic thresholding method. Citrus was separated from the background and the detection of single and multiple fruits was carried out under both downlight and backlight conditions, with a high recognition correctness rate. Traditional target detection algorithms usually rely on features such as the color, texture, and shape of the target and combine morphological operations to segment the image to detect the target region [7; 8]. However, traditional algorithms often struggle to balance real-time and accuracy, and the generalizability of the findings is limited. In contrast, deep learning-based target detection algorithms have more efficient and accurate data representation, are more generalizable, and are easier to apply to real-world scenarios [9]. Among them, first-order detection algorithms (e.g., YOLO) and second-order detection algorithms (e.g., Faster R-CNN [10]) have faster detection speeds than traditional algorithms and are widely used in fruit detection research. [PERSON] et al. [11] proposed an improved Faster RCNN-based prickly peer fruit recognition method. The region of interest pooling (ROI pooling) in the convolutional neural network is improved to the region of interest calibration (ROI align) of the regional feature aggregation method, which makes the target rectangular box of the prickly pear fruit in the natural environment more accurate[PERSON] In order to realize real-time detection and counting, the single-stage target detection method represented by the YOLO series has a wider application in the field of fruit detection. [PERSON] et al.