# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 4 Experimental Results and Analyses
### Visualization of Results  
To further validate the effectiveness of our proposed CIPAM in focusing more effectively on the location of leaf blight disease in complex environments compared to other attention mechanisms, we used the Grad-CAM [48] method to visualize the detection results to see which part of the image the model is most concerned with to make a judgement. Some example images are presented in Figure 11. From the figure, CIPAM can effectively focus more on the location of disease occurrence, even in small and complex scenarios, proving the effectiveness of the CIPAM method compared to other methods.  
During model training, we trained the model in the handheld portion of the dataset because this portion of the dataset is clearer, has more distinct disease features and is more friendly for model training. In addition, we tested the model in the other two parts of the dataset to verify the generalisation ability of our proposed model. The accuracy of our proposed model on the three partial datasets is shown in Table 3.  
Figure 10: Comparison of detection results before and after improvement. (**a**) Original model + CIPAM detection results vs. original model detection results. (**b**) Original model + FRAFM detection results vs. original model detection results. (**c**) Original model + MobileBit detection results vs. original model detection results. (**d**) Original model +CIPAM +MobileBit detection results vs. original model detection results. (**e**) Original model + MobileBit + FRAFM detection vs. original model detection results.  
From the table, we can see that, although the Faster R-CNN model can achieve the highest accuracy in the handheld set, the detection accuracy in the boom set and the drone set decreases significantly by 13.3% and 16.7%. The detection accuracy of the original model in the two parts of the dataset decreases by 13.5% and 10.8%. We analyse that there are large differences in the shooting angle, background and illumination of the three datasets, which cause the model to fail to extract the disease features well.  
Although the accuracy of the model proposed in this paper is not as good as Faster R-CNN in the handheld part of the dataset, the accuracy in the boom set, drone set part of the dataset only decreases by 3.2% and 3.9, which suggests that our proposed model can focus on the location of the disease occurrence more efficiently and extract the features of the disease effectively. It suggests that our proposed model can handle the effect of environmental factors on model performance more effectively and has a stronger generalisation ability. We selected some sample images to show the detection effect on the three datasets, as shown in Figure 12.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Boom Set** & **Drone Set** & **Handheld Set** \\ \hline Origin Model & 69.6\% & 71.3\% & 82.1\% \\ Faster R-CNN & 79.1\% & 75.7\% & 92.4\% \\ Ours & 84.3\% & 83.6\% & 87.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of the three-part dataset.  
Figure 11: Visualization results of different methods. Experimental comparison group SE [49], CBAM, CIPAM can locate disease more accurately than other attention mechanisms, while SE and CBAM are sensitive to the approximate extent of disease location.  
Figure 12: Cont.  
As shown in Figure 12, due to the strong illumination of the images taken by the UAV, the original model has a poor detection capability in the drone part of the dataset, creating a missed detection problem. The original model generates false detections in complex scenarios in the boom part of the dataset. However, CEMLB-YOLOv5 detects the drone and boom portion of the NLB dataset significantly better than the original model.