## 2 Related Work
### Deep Learning Target Detection Methods  
Deep learning theory and technology have experienced rapid development, revolutionizing the field of image processing. Unlike traditional methods, deep learning techniques eliminate the need for manually designed features and are not limited by specific scenes. As a result, deep learning-based target detection algorithms have surpassed conventional approaches in terms of performance. In the realm of deep learning-based target detection, there are two main categories: two-stage and single-stage algorithms. Two-stage algorithms undergo a sequential process of generating candidate regions, followed by the classification and localization of these regions. Although this approach achieves high detection accuracy, it tends to suffer from slower detection speeds. Noteworthy CNN (Convolutional Neural Network)-based algorithms within this category include R-CNN [25], Fast R-CNN [26], Faster R-CNN [27], and Mask R-CNN [28]. R-CNN stands as a pioneering work that introduced convolutional neural networks into target detection. It combines traditional image detection algorithms with deep learning technology. The algorithm starts by generating candidate boxes using the selective search algorithm. These candidates are then passed through a convolutional neural network for feature extraction. Finally, the targets are classified using support vector machines. However, R-CNN is complex to operate, time-consuming, and requires significant computational resources. To address these limitations, GIRSHICK proposed Fast R-CNN. This algorithm integrates a region of interest pooling layer, adjusts the position of candidate regions to align with the feature map derived from the convolutional neural network, and achieves feature extraction across the entire image through feature sharing. These optimizations enable Fast R-CNN to achieve higher detection accuracy than R-CNN while alleviating the abovementioned problems. However, selecting candidate regions remains a time-consuming process in Fast R-CNN. To further improve Fast R-CNN, Faster R-CNN was introduced. It replaces the selective search algorithm with a candidate region network and introduces the concept of a priori frame. By generating multi-scale a priori frames that adapt to targets of different sizes, Faster R-CNN significantly improves the detection speed while maintaining accuracy. Mask R-CNN builds upon the foundation of Faster R-CNNand takes this approach further. It adds a branch that predicts segmentation masks for each region of interest, which enhances the detection accuracy even more. This branch is then merged with the existing branches responsible for classification and incorporating bounding box regression. Despite these optimizations, target detection algorithms that follow a two-stage approach still suffer from many parameters, leading to time-consuming detection processes. Single-stage algorithms, such as SSD [29], RetinaNet [30], CenterNet [31], EfficientDet [32], and the YOLO family of algorithms, simplify the detection process and reduce computation by directly generating category probabilities and positional coordinates for objects. SSD employs a pyramid network structure for target detection. It uses strict coordinate and prediction boxes for non-maximum suppression to achieve accurate target detection and recognition. RetinaNet addresses the issue of class imbalance in dense object detection by introducing the Focal loss function. This loss function places greater emphasis on challenging examples by assigning them higher weights, improving the detection performance. CenterNet represents targets using center points and performs regression to estimate different attributes of the targets based on the specific task, resulting in accurate and effective detection. EfficientDet is based on a scalable neural model architecture and incorporates a novel two-way feature fusion network. This combination strikes a balance between the speed of detection and accuracy, resulting in highly efficient target detection.