## 3 Experiments
### Performance Comparison
#### 3.4.2 Leaf Disease Detection  
Table 2 presents the mAP results for the proposed AE-YOLO and conventional object detection models. In Table 2, the conventional detection models, including the RCNN series and YOLO, selected ResNet50 as the backbone. Except for RetinaNet, library functions of MATLAB (2021a) were used with the same parameter settings to train the conventional object detection models. The optimizer used was SGD [45] with momentum. The epoch number was 100, and the batch size was 4. The learning rate was 0.001, and the momentum term was set to 0.9. The regularization term to prevent overfitting was \(\ell_{2}\)-norm, and its weight was set to 0.0001. The data augmentation technique, including contrast, saturation, and brightness, was applied to increase the training data and prevent overfitting. For more detailed parameter settings, please refer to the author's source code. For RetinaNet, the default settings provided by the source code in [24] were used. The backbone used in RetinaNet was PVT. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **AP (\%) (Marssonina)** & **AP (\%) (Alternaria)** & **mAP (\%)** \\ \hline RCNN [30] & 17.66 & 23.70 & 20.70 \\ \hline Fast RCNN [31] & 46.10 & 38.00 & 42.10 \\ \hline Faster RCNN [32] & 50.01 & 44.90 & 47.50 \\ \hline
**RetinaNet**[44] & 54.60 & 40.20 & 47.40 \\ \hline
**YOLO**[33] & 38.70 & 31.60 & **35.10** \\ \hline
**Proposed attention-enhanced YOLO** & & & \\
**(ResNet50)** & 54.30 & 47.40 & 50.80 \\ \hline Proposed attention-enhanced YOLO & & & \\
**(VGG16)** & 55.00 & 42.40 & 48.70 \\ \hline Proposed attention-enhanced YOLO & & & \\
**(SqueezeNet)** & 51.10 & 47.60 & 49.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation for leaf disease detection. Similarly to the LSA-Net, it should be checked whether the use of the ROI-aware FES and ROI-aware feature fusion can lead to improvements in object detection accuracy. In Figure 4, if the ROI-aware FES and feature fusion are removed, the proposed AE-YOLO becomes identical to the conventional YOLO. By comparing the AP values of the proposed AE-YOLO and conventional YOLO in Table 2, it is known that the proposed AE-YOLO increases the AP value by 15.7% compared to the conventional YOLO. This result confirms that the ROI-aware FES and feature fusions have a significant effect on improving object detection performance. In the last three rows of Table 2, the round bracket indicates the backbone adopted. In the proposed architecture illustrated in Figure 4, various feature extractors can be adopted for the backbone. In this study, three types of feature extractors, ResNet50, VGG16, and SqueezeNet, were tested. The last three rows indicate that the AP values depend on the backbone adopted, and ResNet is the best among the three feature extractors. This indicates that the feature extractors affect the final object detection performance. In the proposed AE-YOLO network, the ROI-aware FES is another feature extractor for spot detection. This ROI-aware FES can enhance the discriminative power of the YOLO feature extractor through ROI-aware feature fusion. This is the reason why the proposed AE-YOLO is superior to the conventional object detection models. In Table 2, it is noted that Fast RCNN and Faster RCNN are not better than the proposed AE-YOLO. The Fast RCNN and Faster RCNN utilize region proposal, whereas the proposed AE-YOLO adopts an additional spot feature extractor, that is, ROI-aware FES. This result indicates that the ROI-aware FES is more effective in improving leaf disease detection than the region proposal. As shown in Table 2, the mAP of RetinaNet was 47.40, and its performance was comparable to that of the Faster RCNN. It is generally known that single-shot detectors are less accurate than two-stage detectors (e.g., Faster RCNN). Figure 8 illustrates the predicted bounding boxes with the proposed AE-YOLO. As illustrated in the figures, the proposed AE-YOLO can detect diseased leaves properly.