## 3 Proposed Method
### Adaptive Feature Enhancement Module  
As small-scale objects typically comprise fewer pixels, the detailed features of these small objects are often lost during the feature extraction process of the backbone network. Currently, most detection algorithms only focus on detecting small objects by utilizing  
Figure 3: The architecture of the proposed backbone network. Figure 2: The overall structure of the AFE-YOLOv8 network. shallow feature maps in their frameworks. Although shallow feature maps contain detailed feature information about small objects, they also include unnecessary background features, leading to significant redundant computation during the detection process and a slower inference speed of the model. [PERSON] et al. (2019) and [PERSON] et al. (2019) proposed that deep feature maps contain more semantic information than shallow feature maps. However, deep feature maps have fewer pixels and larger receptive fields, which makes them less suitable for directly detecting small objects in the deep feature layer. Therefore, we propose the Adaptive Feature Enhancement Module (AFEM) to enhance the features of the small-object pipeline by incorporating features from both the medium-object pipeline and the large-object pipeline. By integrating the small-object information from varying-scale-object pipelines into the feature maps of the small object pipeline, we can enrich the feature maps of the small-object pipeline with more detailed and semantic information. The underlying idea is that the contextual semantic information in the deep feature map can be utilized to guide shallow feature learning, thereby enhancing the details and semantic features of small objects through context modeling. In this manner, the AFEM enhances the saliency of features from small objects compared to those from the background, leading to improved accuracy in detecting small objects during the feature fusion process. The structure of the AFEM is illustrated in Figure 4.  
Considering that deep layers predominantly contain more features from large-scale and medium-scale objects, they inherently possess less semantic information related to small objects. Consequently, it becomes imperative to assign lower weights to features from deep layers in order to enhance the features of small objects. Taking this into account, we introduce a learnable adaptive fusion weighting factor during the feature enhancement process. The learnable adaptive fusion factors, denoted as \(\alpha\), \(\beta\), and \(\gamma\), are subject to the constraint that their sum is equal to 1. Specifically, \(\alpha\) represents the contribution ratio of feature aggregation from the \(F_{1}\) layer, \(\beta\) represents the contribution ratio of feature aggregation from the \(F_{2}\) layer, and \(\gamma\) represents the contribution ratio of feature aggregation from the \(F_{3}\) layer. The implementation of this method consists of three steps. Firstly, we apply conv \(1\times 1\) and downsampling operations to the shallow feature map \(F_{1}\), resulting in the feature map \(F_{1d}\) with the same size as the \(F_{2}\) layer. Then, we multiply the \(F_{2}\) layer by a learnable factor  
Figure 4: The architecture of the proposed Adaptive Feature Enhancement Module (AFEM). \(\beta\) through a conv \(1\times 1\) operation and fuse it with \(F_{1d}\), yielding the fused feature map \(F_{12}\). The implementation formula is as follows:  
\[F_{12}=\text{Conv}(F_{2})\cdot\beta+\text{Downsample}(\text{Conv}(F_{1})) \tag{1}\]  
The second step involves the \(F_{3}\) layer, which contains more semantic information. Convolution upsampling operations are performed, and then the results are multiplied by the learnable factor \(\gamma\). The result is added to \(F_{12}\) for addition fusion, resulting in \(F_{23}\).