## 2 Related Works  
### General Object Detection  
The general object detection models, which primarily comprise deep learning and traditional models, have the potential to detect multiple object classes in various scenes. In the last decade, there has been significant progress in object detection, particularly in deep learning-based models. These models typically employ CNNs to extract features and classify images, thereby achieving an accurate localization and classification of the target objects. Vision detectors based on deep learning can be broadly categorized into two categories: one-stage models and two-stage models. The two-stage models appeared earlier, mainly including R-CNN [9], Fast R-CNN [10], Faster R-CNN [11], etc. The R-CNN, initially introduced in 2014, is a CNN-based method that utilizes the selective search algorithm for candidate box extraction and performs convolution for feature extraction in both classification and regression tasks. To accelerate the detection process of the R-CNN, [PERSON] et al. introduced Fast R-CNN. This method introduced an ROI pooling layer, enabling the feature extraction and classification of multiple regions of interest (ROIs) in a single forward pass. Additionally, a multitask loss function was employed to optimize both classification and regression tasks, resulting in an improved detection performance. Subsequently, Faster R-CNN was introduced, incorporating the Region Proposal Network (RPN) [11] to replace the selective search algorithm. The RPN allowed for the direct learning of candidate box coordinates and categories from extracted feature maps, facilitating real-time object detection. Moreover, it effectively addresses the challenges posed by multi-scale variations, which previous algorithms struggled to handle. The demand for real-time object detection has accelerated the rapid development of one-stage object detection. One-stage models mainly include SSD [24], RetinaNet [18], the YOLO series [13; 14; 15; 16; 17], etc. The SSD algorithm revolutionized the detection task by formulating it as a regression problem and incorporating a feature pyramid to enable accurate object predictions on feature maps with varying receptive fields. RetinaNet tackles the challenge of imbalanced positive and negative samples in one-stage detectors. It introduced a novel loss function called Focal Loss, which assigns appropriate weights to positive and negative samples. This innovation allows RetinaNet to achieve both faster processing times than other one-stage detectors and a higher accuracy than two-stage detectors. Figure 1: The pipeline of the proposed method, and the targets in the red circle are YOLOv8 missed detections. The main contributions of our approach include the AFEM, the RFEM, and the improved backbone with the MNFM. The YOLO (You Only Look Once) algorithm is a real-time object detection technique that treats the entire image as the network input to predict the class and location of multiple objects directly. YOLOv2 [14] improved the detection accuracy through the incorporation of a deeper network structure, Batch Normalization, Anchor Boxes, and other techniques. Building upon YOLOv2, YOLOv3 [15] further enhanced the algorithm by introducing multi-scale detection and Darknet-53 features to improve in performance. YOLOv4 [16] implemented new technologies such as CSPDarknet53, SPP Block, a novel neck, and an innovative head based on YOLOv3. YOLOv5 incorporates techniques such as lightweight models, data augmentation, and adaptive training strategies for faster detection and higher accuracy. The YOLOv6 model implements optimizations including the application of deeper convolutional neural networks, the addition of data augmentation techniques, and the adoption of smaller anchor boxes. Compared to previous versions, YOLOv7 [17] adopts a deeper network structure and introduced several new optimization techniques to improve the detection accuracy and speed.