## 2 Related Works  
In recent years, target detection models based on deep learning have achieved notable progress in computer vision tasks. As an end-to-end object detection algorithm, YOLO (You Only Look Once) is commonly used in object recognition and detection tasks for its efficiency and reliable performance. Substantial academic progress has been made in optimizing YOLO-based models. For instance, [PERSON], [PERSON] et al. [4] proposed SB-YOLO-V8, a convolutional neural network based on YOLO optimized for real-time human detection in agricultural and urban environments. By incorporating Binary Adaptive Learning Optimization (ALO) and the Synthetic Minority Over-sampling Technique (SMOTE), the model effectively addresses data scarcity and class imbalance while enhancing feature extraction capability. [PERSON], [PERSON] et al. [5] introduce LUD-YOLO, a lightweight YOLOv8-based detection algorithm. It is designed to improve object detection in UAV autonomous missions. This method tackles the challenges of complex backgrounds, small objects, and occlusion. Experiments conducted on the VisDrone2019 and UAVDT datasets demonstrate its effectiveness, with ablation and comparative results confirming that the improved LUDY-N and LUDY-S models perform well across multiple evaluation metrics, demonstrating robustness and generalization capability. Similarly, [PERSON] et al. [6] develop a coal-rock interface recognition model by integrating the YOLO real-time detection algorithm with a lightweight bilateral segmentation network. The model employs a regional similarity transformation function and the Dragonfly Algorithm to improve image quality and boundary segmentation in coal-rock scenarios. Comparative experiments with three benchmark models confirm its advantage in edge inference tasks, supporting intelligent coal mining development. [PERSON], [PERSON] et al. [7] propose DM-YOLO, an improved tomato leaf disease detection method based on YOLOv9. The backbone network incorporates a lightweight dynamic upsampling module (DySample) to extract features from small lesions better and reduce background interference. Additionally, the MPDIoU loss function improves the localization of overlapping lesion boundaries, leading to more accurate detection. [PERSON] et al. [8] propose AHC-YOLO, a YOLO-based algorithm for detecting the hydrophobicity of composite insulators. The model incorporates a high-performance GPU network (HGNetv2), a Mixed Local Channel Attention mechanism (MLCA), a lightweight convolutional module (CSPPC), and the Inner-WIoU loss function. These components help reduce computational overhead and improve accuracy in skirt detection and hydrophobicity grading, making the model suitable for practical engineering use. [PERSON] et al. [9] develop a lightweight YOLO-based framework for skin cancer detection to improve early diagnosis accuracy. The study evaluates several models, including YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv8s, on the ISIC dataset for detecting and classifying nine types of skin cancer. The experimental results indicate that YOLOv5s outperforms in specific categories, while YOLOv8s achieves higher accuracy in others. It demonstrates the framework's efficiency and deployment potential in embedded systems. He, [PERSON] et al. [10] propose an intelligent recognition algorithm for ferrography wear particles based on the convolutional block attention module (CBAM) and YOLOv5 to address challenges such as image blurring, complex backgrounds, particle overlap, and insufficient illumination in ferrography images. The CBAM module was integrated to strengthen the feature representation of wear particles and increase the model's focus on relevant regions. [PERSON]. [PERSON] et al. [11] propose a novel hierarchical model aimed at addressing the limitation of ViT's single-scale, low-resolution feature maps, which tend to lose fine-grained semantic information. The method adopts a convolutional vision Transformer as the backbone for feature extraction and fusion.