# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 2 Materials and Methods
### Proposed Model: BCS_YOLO
#### 2.2.2 HLFFE Module  
In the natural environment of corn fields, leaf lesions usually present characteristics of blurred edges and irregular shapes. To improve the model's capacity for processing these intricate characteristics, the newly designed HLFFE module decomposes features through high-frequency and low-frequency branches to separately strengthen detail capturing and global contour perception, and simultaneously preserve local textures and boundary structures. This strengthens the model's capability for extracting distinct features from intricate scenes. High-frequency information focuses on key local details such as edges, textures, and fine structures. Low-frequency information emphasizes capturing global context, overall shapes, and background elements. This provides a foundation for semantic understanding. Through targeted enhancement of information in both high-frequency and low-frequency domains, HLFFE achieves more comprehensive feature representation.  
The structure diagram of the HLFFE module is shown in Figure 4. For input feature map \(X\), the HLFFE module first decomposes it into high-frequency and low-frequency components. This decomposition is achieved through an average pooling operation (\(AvgPool_{2\times 2}\)) followed by a difference operation:  
\[X_{in}^{low}=AvgPool_{2\times 2}(X) \tag{6}\]  
\[X_{in}^{high}=X-Interpolate\left(X_{in}^{low}\right) \tag{7}\]  
where _Interpolate_ denotes using bilinear interpolation to upsample \(X_{in}^{low}\) to match the original dimensions.  
In the low-frequency branch, the Multi-scale Edge Enhancement Module (MEEM) [34] is used to further improve expression quality for blurred contour areas. As shown in Figure 4, MEEM extracts multi-scale edge features through sequential average pooling and edge enhancement (EE) operations. EE enhances the edge of pest and disease information at each scale to highlight its key boundary areas. For inputs of different scales, the EE process can be expressed as follows:  
\[X_{i+1}=X_{i}+\sigma\big{(}Conv\left(X_{i}-AvgPool_{3\times 3}\left(X_{i}\right) \right)\big{)} \tag{8}\]  
Figure 4: The newly designed HLFFE module, utilized by the proposed BCS_YOLO model.  
Finally, the enhanced low-frequency features are unsampled to match the original dimensions.  
In the high-frequency branch, CMUNeXtBlock [35] is used to enhance detail information. CMUNeXtBlock uses a series of residual blocks with depth convolution, with large convolution kernels to effectively capture spatial relationships. Each residual unit in CMUNeXtBlock performs the following operations:  
\[X_{out}^{high}=BN(GELU(W_{2}\cdot BN(GELU(W_{1}\cdot BN(GELU(D_{k}(X_{in}^{high})) ))))) \tag{9}\]  
where \(D_{k}\) denotes a depth convolution with kernel size \(k\), \(W_{1}\) and \(W_{2}\) denote \(1\times 1\) convolutions for channel expansion and reduction, respectively, \(BN\) denotes batch normalization, and \(GELU\) denotes the Gaussian Error Linear Unit activation function.  
At the end, the enhanced high-frequency features \(X_{out}^{high}\) and low-frequency features \(X_{out}^{low}\) are concatenated along the channel dimension and pass through a channel attention (\(CA\)) module to reduce dimensions, further refine the features, and adaptively recalibrate the channel feature responses.  
By decomposing and separately processing high-frequency and low-frequency components, the HLFFE module effectively enhances both fine details and global structures in visual features. The adaptive fusion mechanism ensures that the model can selectively emphasize the most relevant information, thereby improving its performance in tasks demanding both precise detail recognition and contextual understanding. This balanced approach to feature enhancement helps improve the model performance in detection tasks without introducing excessive computational complexity.