## 3 Method
### Enhanced Feature Pyramid Network  
In preparation for feature fusion within the FPN, a \(1\times 1\) convolutional layer is employed. This step serves to transform the channel dimensions, facilitating the subsequent aggregation of feature maps from different scales. The outcome is a fused feature map endowed with enriched information. However, a commonly employed fusion strategy involves channel dimension reduction in higher-level features via convolutional layers, enabling their summation with lower-level feature maps. This process, unfortunately, introduces information loss within feature maps originating from varying scales. Additionally, the semantic gap between feature maps generated at different scales is substantial. If the model neglects these notable semantic disparities and directly applies \(1\times 1\) convolutions followed by addition for multi-scale information fusion, there remains room for improvement within this fusion strategy. It is essential to acknowledge that the fundamental design principle of the FPN involves fusing high-level features with their low-level counterparts, thereby empowering high-level features to enhance low-level feature representation by leveraging semantic insights from larger scales. Nevertheless, the feature map located at the highest level lacks a corresponding higher-level, larger-scale feature map for fusion. Consequently, the original network architecture generates a feature map at the highest level, corresponding to the largest scale, devoid of input from features at other scales. Instead, this map is directly subjected to channel dimension reduction before being merged with feature maps from lower scales. This approach seems inequitable to the highest-scale feature map, especially considering that lower-scale feature maps have already undergone fusion with features from more elevated scales. The approach proposed in this paper effectively tackles the limitations of the FPN utilized in the Sparse R-CNN network to extract multi-scale features. It incorporates mechanisms for enhancing both top-level and bottom-level features, thereby facilitating the extraction of multi-scale image features that encompass both fine-grained and coarse-grained details. This approach better satisfies the demands of object detection. Given an input image, the process of multi-scale feature extraction benefits from the top-level and progressive feature enhancement mechanisms, resulting in the extraction of more finely fused and higher-quality multi-scale feature maps with minimized information loss. Subsequently, the detection network processes these multi-scale features to generate object classification scores and bounding box coordinates, which collectively constitute the foundation for the final object detection outcomes. The architecture of the feature extraction network is depicted in Figure 1. It encompasses two principal stages: top-level feature enhancement and progressive feature fusion across multiple scales. During the first stage, top-level feature enhancement leverages the multi-scale features obtained from the backbone network. These features are integrated into the ultimate output network features. By employing a reduction in channel dimensionality, the extent of information loss in top-level features is ameliorated. The subsequent stage involves progressive feature fusion across multiple scales during the multi-scale feature fusion phase. This approach effectively diminishes the semantic gap existing between feature maps of varying resolutions within the feature pyramid's multi-scale feature fusion process. Consequently, this strategy contributes to more refined multi-scale feature fusion. The combination of these two stages culminates in the creation of well-integrated multi-scale feature maps. During the stage of multi-scale feature extraction, the initial input image undergoes processing through the backbone network, which is constructed based on the ResNet-50 architecture.