## 1 Introduction
The overall framework is divided into three parts: backbone, neck, and head. The backbone serves as the main body of the entire structure, tasked with feature extraction. Common backbone  
Figure 1: Morphology of the ureteral orifice that is challenging to identify in ureteroscopy, where (**a–d**) represent images of actual scenes after cropping, and (**e–h**) are their corresponding images with annotated ureteral orifices. networks include VGG [1], ResNet [2], DenseNet [3], MobileNet [4], EfficientNet [5], and CSPDarknet53 [6], among others. These networks have proven strong feature extraction capabilities in classification and other tasks. Researchers often fine-tune the backbone to make it more suitable for specific tasks. The neck section is used to enhance resolution by outputting features of different scales to fully utilize feature information. Common neck architectures include FPN [7], PANet [8], NAS-FPN [9], BiFPN [10], ASFF [11], SFM [12], and others. The head is the classification network, primarily responsible for prediction and classification. It is generally divided into two types: one-stage object detectors and two-stage object detectors. Historically, two-stage detectors have been more accurate, with a series of algorithms represented by RCNN [13; 14; 15], including Fast-RCNN and Faster-RCNN, performing excellently in terms of precision. In comparison to two-stage detectors, one-stage detectors simultaneously predict bounding boxes and target categories, showing a clear speed advantage but lower accuracy. Representative models for one-stage detectors include the YOLO series and SSD series. To enhance the ability of SSD to express low-level feature maps, DSSD [16] uses ResNet101 as the backbone, and FSSD [17] integrates low-level features into the high-level features based on SSD. The YOLO series [18] has evolved into version 8, seeking a balance between accuracy and performance. Although CNN-based networks [19] hold a significant position in the field of object detection in deep learning, with the migration of transformers from NLP (Natural Language Processing) to CV (Computer Vision), more object detection methods are adopting updated perspectives to address problems. Models such as Swin-Transformer [20] and DETR [21] inject new vitality into object detection tasks, and further research on attention mechanisms opens up more possibilities for development in the CV field, such as BAM [22], CBAM [23], SCConv [24], ECAttention [25], SimAM [26], Biformer [27], SE [28], and other modules, which not only enhance network performance but also integrate network architecture design more effectively. Currently, object detection has been widely applied in various real-world scenarios such as autonomous driving, robot vision, video surveillance, etc. In the field of medical clinical applications, there is rich research accumulation. Regarding colorectal polyps, [PERSON] optimized polyp detection by combining image enhancement in the HSV color space and a dual-path convolutional network. Through image enhancement, the feature quality of the dataset is strengthened. Simultaneously, the CNN network, which performs parallel processing of convolution kernels of different scales and then unifies channel-fused features, also improves the model's ability to extract features [29]. [PERSON] created a lightweight object detection network based on SSD and MobileNet, highlighting its advantages in real-time performance after extracting features using the lightweight MobileNet backbone and detecting target positions using anchor-similar Prior Boxes [30]. [PERSON] et al. [31] made modifications to the YOLO model, including scaling and activation function transformations, effectively improving the network's performance in corresponding scenarios. Meanwhile, in tasks such as gastrointestinal disease classification [32; 33], polyp segmentation, and tumor judgment [34; 35], deep learning methods have been widely researched and applied. In urological surgery, deep learning has also been preliminarily studied.