# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 4 Experimental Results and Analyses
### Analysis and Comparison of Experimental Results  
We evaluate the superiority of the proposed CEMLB-YOLO algorithm in detecting maize leaf blight in complex field environments using other object detection algorithms as comparison experiments. Table 1 compares the detection effectiveness of CEMLB-YOLO with other models. In this paper, we replaced the backbone network in YOLOV5 with MobileNetv3 as the original model.  
Based on the results of the comparative experiments in this paper, the proposed model performs well in detecting maize leaf blight in complex environments in the field. Compared to YOLOv3-tiny and YOLOv7-tiny, which have lower parameter quantities and complexity, our model proposed in this paper has lower parameter and model complexity but higher accuracy. Compared to YOLOv3-tiny, the accuracy of the model is improved by 11.3%, and the model and parameter amount is reduced by 4 Million, 2.5 GFLOPs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Imgsize** & **mAP** & **FPS** & **Parameters (m)** & **GFLOPs** \\ \hline Origin Model & 640 \(\times\) 640 & 82.1\% & 84 & 6.2 & 6.6 \\ YOLOv3-tiny & 640 \(\times\) 640 & 76.2\% & 65 & 8.6 & 12.9 \\ YOLOv7-tiny & 640 \(\times\) 640 & 81.3\% & 64 & 6.02 & 13.16 \\ Faster R-CNN & 640 \(\times\) 640 & 92.4\% & 32 & 41.13 & 78.1 \\ RetinaNet & 640 \(\times\) 640 & 91.6\% & 36 & 36.3 & 82 \\ YOLOv8 & 640 \(\times\) 640 & 89.4\% & 69 & 11.1 & 28.06 \\ YOLOv8n & 640 \(\times\) 640 & 78.7\% & 77 & 3.01 & 8.2 \\ YOLOX [45] & 640 \(\times\) 640 & 91\% & 53 & 8.94 & 26.64 \\ Song [46] & 512 \(\times\) 512 & 82.1 & 39 & 40.9 & / \\ Sun [47] & 512 \(\times\) 512 & 91.8 & 28 & / & / \\ Ours & 640 \(\times\) 640 & 87.5\% & 62 & 4.54 & 9.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison experiments with other models.  
The accuracy of our model is 6.2% higher than YOLOv7-tiny, and the model parameter amount and complexity are reduced by 1.5 Million, 3.7 GFLOPs. During the comparison experiments, we chose Resnet50 as the backbone network of Faster R-CNN and RetinaNet, which results in a higher complexity and a higher number of parameters of the model. The number of parameters and complexity of our model is one-tenth of the Faster R-CNN, RetinaNet, and the accuracy has been reduced by 4.9% and 4.1%. Compared with the emerging YOLOv8s and YOLOX, our model complexity is reduced by nearly 2/3, and the accuracy is only 1.9% less than YOLOv8s and 3.5% less than YOLOX. Compared to YOLOv8n, which has a similar number of model parameters and complexity, the accuracy of our model increases by 8.8%, while the number of parameters increases by only 150 w and 1.2 GFLOPs. We also compared our approach with other researchers, and our model is 5.4% more accurate than [PERSON]'s and 4.3% less accurate than [PERSON]'s approach.  
In this paper, FPS is introduced to evaluate the impact of different methods on the detection speed. From the table, we can see that our proposed model's detection speed is lower than YOLOv3-tiny, YOLOv7-tiny, YOLOv8n and YOLOv8s, which we analysed due to the introduction of MobileBit. Our model's detection speed is faster than YOLOX, RetinaNet, YOLOv7 and Faster RCNN models; particularly, the detection speed of our model is roughly twice as fast as the detection speed of Faster RCNN, RetinaNet. The detection speed of our model also shows a superior performance when compared to the detection speed of other researchers' methods.  
Overall, our model achieves a better balance between detection speed and detection accuracy, and our model is more suitable for detecting maize leaf blight in complex environments.