## 3 The Proposed Method
### Multi-Scale Feature Fusion  
The multi-scale features retrieved by the CSPMarknet53, symbolized as \(\{P_{i}\}\), \(i\in\{3,4,5,6,7\}\), where each \(P_{i}\) represents a feature map at a different scale, are integrated by the multi-scale feature fusion module using six BiFPN layers. In this case, the greatest resolution is represented by \(P_{3}\) and the lowest by \(P_{7}\). The BiFPN layer uses these features as input, processes them, and fuses them to improve object representation at various sizes. To strike a compromise between computing efficiency and semantic richness, the BiFPN begins at \(P_{3}\) and concludes at \(P_{7}\). Feature maps from \(P_{3}\) to \(P_{7}\) avoid the high computational costs associated with lower-level features (such as \(P_{1}\) and \(P_{2}\)) while capturing the high-level semantic information required for tasks such as occlusion removal. This design optimizes overall performance and feature integration by aligning with the CSPMarknet53 backbone. The feature maps \(P_{3}\) to \(P_{7}\) have the following dimensions:  
\[P_{3}\in\mathbb{R}^{\frac{H}{M}\times\frac{W}{R}\times C_{3}},\;P_{4}\in \mathbb{R}^{\frac{H}{R}\times\frac{W}{R}\times C_{4}},\;P_{5}\in\mathbb{R}^{ \frac{H}{M}\times\frac{W}{R}\times C_{5}},\;P_{6}\in\mathbb{R}^{\frac{H}{M} \times\frac{W}{R}\times C_{6}},\;P_{7}\in\mathbb{R}^{\frac{H}{128}\times\frac {W}{128}\times C_{7}}\]  
where \(C_{k}\) represents the channel depth of each feature map. These multi-scale features are effectively fused across BiFPN layers to enhance the model's ability to perform occlusion removal, capturing both low-resolution and high-level semantic features, as well as finer details. The BiFPN architecture as shown in Figure 3 consists of several key components: lateral connections, weighted bidirectional feature fusion process, and both bottom-up and top-down pathways to combine features from different scales. This bidirectional strategy gathers both high-level semantic information and low-level details, ensuring comprehensive feature fusion. In the lateral connection phase, each input feature map is processed through a \(1\times 1\) convolution to adjust its channel dimensions. This step is crucial for ensuring that feature maps from different scales can be effectively combined. The operation is defined as  
\[P_{\text{latk}}=\text{Conv}_{1\times 1}(P_{k})\]  
This transformation enables the integration of features from different scales while preserving their spatial information. In the bottom-up and top-down pathways, each BiFPN layer uses separable convolution blocks in both upward and downward paths to process and integrate features. Top-Down: To match the resolution of higher-resolution maps, low-resolution feature maps are upsampled. The process is outlined below:\[\text{Upsample}(P_{i})\rightarrow\text{Convolution}_{\text{up}}\rightarrow \text{Feature}_{\text{up}}\]  
where \(\text{Convolution}_{\text{up}}\) is a separable convolution block applied after upsampling. Bottom-Up: In order to match the resolution of lower-resolution maps, high-resolution feature maps are downsampled. The process is outlined below:  
\[\text{Downsample}(P_{\text{lat}})\rightarrow\text{Convolution}_{\text{down}} \rightarrow\text{Feature}_{\text{down}}\]  
where \(\text{Convolution}_{\text{down}}\) is a separable convolution block applied after downsampling. Each separable convolution block consists of (1) depthwise convolution, which operates on each input channel separately; (2) pointwise convolution, which merges the depthwise convolution's outputs and (3) batch normalization and swish activation, which are applied to the outputs of the pointwise convolution to normalize and activate the features. The output feature map \(F_{\text{sep}}\) from the separable convolution blocks is defined as  
\[F_{\text{sep}}=\text{DepthwiseConv}(\text{PointwiseConv}([P_{i}])) \tag{8}\]  
where \([P_{i}]\) are the input feature maps to the BiFPN layer.