## 1 Introduction
Deep-learning-driven object detection algorithms present remarkable advantages over their traditional counterparts, excelling in terms of precision and efficiency, thereby positioning themselves as the leading approach in the field of object detection. The integration of deep learning into object detection has led to effective resolutions for many of these challenges. In 2012, [PERSON] et al. [4] introduced the groundbreaking AlexNet model, achieving a substantial breakthrough in the ImageNet image recognition competition. Following this milestone, convolutional neural networks (CNNs) have gradually gained prominence in the realm of object detection, forming the cornerstone of computer vision models. With each passing year, numerous researchers have contributed to the development of diverse network models that exhibit enhanced performance in both accuracy and speed for image recognition tasks. Deep-learning-based object detection algorithms predominantly fall into two distinct categories: one-stage algorithms and two-stage algorithms. Among these, the two-stage algorithms initially extract features from the input image, subsequently conducting object classification and bounding box regression on selected candidate regions. The inception of the two-stage algorithm era was marked by the introduction of the R-CNN (regions with CNN feature), pioneered by [PERSON] et al. [5], which marked a pioneering breakthrough in the realm of object detection. The R-CNN approach ushered in the integration of convolutional neural networks (CNNs) into object detection, reshaping the landscape of this domain. The R-CNN method incorporated the Selective Search algorithm to generate a set of candidate regions. Subsequently, these regions were resized to a standardized dimension and fed into a neural network for feature extraction. This network was fine-tuned based on a pretrained ImageNet model tailored to the specific object detection task. Despite achieving commendable outcomes when compared to traditional methodologies of that time, the R-CNN approach faced certain limitations. Notably, the presence of fully connected layers hindered its accuracy when confronted with deformations, and its adaptability to varying image sizes remained constrained. The challenges were addressed by [PERSON] et al. [6] through the introduction of the SPPNet (Spatial Pyramid Pooling Network), a pioneering approach that brought about the concept of spatial pyramid pooling. This innovation facilitated the model's ability to seamlessly adapt to images of varying sizes. Subsequently, [PERSON] [7] presented the Fast R-CNN framework, leveraging the VGG network as the foundational architecture to enhance both the computational speed and precision of object detection. Further building upon the Fast R-CNN foundation, Faster R-CNN, as introduced by [PERSON] et al. [8], incorporated the Region Proposal Network (RPN) for the generation of candidate bounding boxes. This innovation significantly improved both the accuracy and speed of the approach by replacing the resource-intensive selective search step with a more efficient candidate box generation mechanism. The Faster R-CNN methodology employed a shared feature map to simultaneously handle the tasks of classification and regression, integrating it seamlessly into the RPN for the generation of candidate bounding boxes. [PERSON] and [PERSON] [9] introduced the Cascade R-CNN, a novel approach that involved training with varying thresholds to enhance the accuracy of object detection. It is important to note that while two-stage algorithms typically offer superior accuracy by initially predicting potential object locations based on input features and subsequently conducting object classification and bounding box regression for more precise classification and positioning, they often exhibit slower processing speeds. Recognizing this challenge, one-stage algorithms such as YOLO (You Only Look Once), proposed by [PERSON] et al.