# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 3 Cemlb-Yolo Network Model
### Crucial Information Position Attention Mechanism  
Maize leaf blight occurs in small and dense areas; some critical information is lost or blurred during the image feature extraction model's down-sampling procedure, impairing the model's detection capability.  
To address this issue, we propose a Crucial Information Position Attention Mechanism (CIPAM) that helps the model to be able to focus on specific regions of important details and highlight the most informative regions. The model can retain and utilise the most  
Figure 5: Schematic representation of the CARAFE up-sampling operator structure.  
Figure 6: The overall architecture of the CAM and SAM. (**a**) is CAM that is used for \(F_{H}\). (**b**) is SAM used for \(F_{L}\). The \(F_{L},F_{H}\); respectively, they represent low feature maps and deep feature maps.  
essential parts of the image even during the down-sampling process, reducing the potential loss of critical information. The structure is shown in Figure 7.  
For the input feature map \(X\in R^{C\times H\times W}\), CIPAM first constructs interdependencies between channels of the feature map using a self-attention approach. Specifically, the feature map of the \(i^{th}\) channel is then reweighted and fused with the feature maps of other channels based on their correlation coefficients to enable information interaction between different channels and enhance the representation of crucial information. For the \(i^{th}\) channel of the feature map, it is as follows:  
\[Weight_{ij}=\frac{\exp(Q_{i}\cdot{K_{j}}^{T})}{\sum_{i=1}^{C}\exp(Q_{i}^{T} \cdot{K_{j}})} \tag{11}\]  
\[X_{i}^{\prime}=\sum_{j=1}^{C}Weight_{ij}\cdot{V_{j}} \tag{12}\]  
\(Weight_{ij}\) denotes the correlation of the \(i^{th}\) channel with the \(j\)-th channel.  
More importantly, in the complex and ever-changing natural environment, accurately pinpointing the location of plant diseases is crucial for enhancing the model's performance. In this article, we use CA [39] to capture the exact position. Specifically, for input feature maps \(X\in R^{C\times H\times W}\), two one-dimensional pooling layers are used along the horizontal and vertical directions, respectively, to obtain \(Z^{h},Z^{w}\). The \(Z^{h},Z^{w}\) captures both long-range dependencies and retains precise positional information of crucial information. \(Z^{h},Z^{w}\) can be expressed mathematically as follows in Equations (13) and (14):  
\[Z^{w}_{c}=\frac{1}{H}{\sum_{0\leq i\leq H}X_{c}^{\prime}(i,w)} \tag{13}\]  
\[Z^{h}_{c}=\frac{1}{W}{\sum_{0\leq i\leq W}X_{c}^{\prime}(h,i)} \tag{14}\]  
The two directional feature maps are concatenated and passed through a \(1\times 1\) convolution layer to obtain the feature map F, representing the interaction between the height and width directions. After applying batch normalization and a non-linear activation function, the feature maps are split into two directional feature maps \(f^{w},f^{h}\). Then using the Sigmoid function to obtain the weights \(g^{w},g^{h}\) of the feature maps in height and width. Finally, the  
Figure 7: CIPAM structure. Each channel feature map is regenerated and then uses horizontal and vertical pooling layers to capture the generated key position information.  
weights are multiplied by \(X^{\prime}\). The process uses mathematical expressions as shown in Equations (15)-(18):  
\[F=\sigma(Conv(\left[Z^{h},Z^{w}\right])) \tag{15}\]  
\[g^{h}=\sigma(Conv(f^{h})) \tag{16}\]  
\[g^{w}=\sigma(Conv(f^{w})) \tag{17}\]  
\[X^{out}_{c}=X^{\prime}_{c}(i,j)\times g^{h}(i)\times g^{w}(j) \tag{18}\]  
The resulting feature map \(X^{out}\) significantly enhances the representation of crucial information in the feature map and accurately captures the location of such information. Our experiments demonstrate that our proposed strategy focuses better than previous attention techniques on the location of disease occurrence in complex field environments.