# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 3 Experimentation and Performance Evaluation
### Experimental Results and Analysis  
The quantitative analysis of all methods in this paper is conducted based on the same indicators and the same dataset.  
The mean average precision (mAP) is an essential performance evaluation metric for target detection models. The mAP here is the average of the average precision (AP) of detection calculated for each category when IOU = 0.5. The experimental results are shown in Table 2, and the MFF-CNN has the best performance, in terms of mAP, compared to the others' maize disease detection. It is 10.4% higher than Faster R-CNN and 5.3% higher than CenterNet, and it also outperforms YoLov5s and DETR.  
There are two main reasons that the MFF-CNN achieves the best detection performance in maize leaves. One is that the CA channel attention module enhances the feature information of the detected targets. In particular, it enhances the feature information of small targets, overlapping obscured targets and fuzzy targets, which makes the detection better. Another reason is the application of Softpool, which uses down-sampling to reduce the amount of data while retaining as much feature information as possible, thus preventing the loss of maize leaf detection information with blurred edges and corners. These two modules significantly improve the detection accuracy and efficiency of the MFF-CNN model in maize leaf detection.  
To verify the superiority in temporal performance of the MFF-CNN, we use detection time, i.e., the time to complete an image, to compare the time performance. To get the detection time, we loop through 1000 corn leaf images for detection and then calculate the average inference time between one image input to the network model and the output model. The detection time of the MFF-CNN is 0.039 s, which is not as good as YoLov5s, but its detection efficiency is faster than DETR, CenterNet, and Faster RCNN by at least 0.37 s. Since the FOCUS module reduces the image size and the computation, it greatly reduces the MFF-CNN detection time. However, as the MFF-CNN also adds a CA module and an SSP module on top of the YoLov5s network, the detection time of the MFF-CNN algorithm is slightly longer than YoLov5s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Mean Average Precision (mAP)** & **Detection Time/s** & **FLOPs** \\ \hline MFF-CNN & 0.486 & 0.039 & 4.2 \\ YoLov5s & 0.47 & 0.017 & 16.5 \\ DETR & 0.467 & 2.054 & 76.5 \\ CenterNet & 0.433 & 1.222 & 34.97 \\ Faster RCNN & 0.382 & 0.409 & 256.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of mAP, detection time, and FLOPs.  
We use floating point operations (FLOPs) to test the algorithm's complexity. The value of the FLOPs for the MFF-CNN is 4.2 G, which is much lower than the other algorithms, indicating little model complexity and a minimal computational effort.  
In the detection of plant leaf diseases, missing detection is one of the important factors affecting the accuracy of disease detection. This section uses sensitivities to test the missed detection rate of the classifier model [48; 49]. Sensitivity describes the ratio of true positive (TP) to actual positive (TP plus FN), where FN is a false negative. The higher the sensitivity, the lower the missed detection rate.  
Table 3 compares the performance of the proposed MFF-CNN versus the other algorithms in terms of sensitivities (%) at various false positives (FPs) per image.  
The numbers 0.5, 1, 2, and 4 represent the different values of FPs. As can be seen from the table, when IOU = 0.5 and there are four false positives in each picture, the sensitivity of our algorithm is 3.16% higher than Yolov5s and 9.83% higher than Faster RCNN. The results show that the MFF CNN has the highest sensitivity, i.e., the lowest miss detection rate, and it exhibits the optimal detection performance.  
We analyze several specific cases in the following sections.