Rice Science ›› 2023, Vol. 30 ›› Issue (6): 652-660.DOI: 10.1016/j.rsci.2023.06.005
• • 上一篇
收稿日期:
2023-02-28
接受日期:
2023-06-30
出版日期:
2023-11-28
发布日期:
2023-08-10
. [J]. Rice Science, 2023, 30(6): 652-660.
Backbone | Training time (h) | Box loss | Mask loss | Classification loss | Segmentation loss |
---|---|---|---|---|---|
ResNet50 | 60.88 | 0.024 | 0.024 | 0.069 | 0.022 |
ResNet101 | 36.22 | 0.026 | 0.026 | 0.083 | 0.022 |
Swin-Tiny | 30.23 | 0.097 | 0.065 | 0.040 | 0.044 |
Swin-Base | 38.27 | 0.067 | 0.044 | 0.027 | 0.034 |
Table 1. RiceblastSegMask training parameters using different backbone networks.
Backbone | Training time (h) | Box loss | Mask loss | Classification loss | Segmentation loss |
---|---|---|---|---|---|
ResNet50 | 60.88 | 0.024 | 0.024 | 0.069 | 0.022 |
ResNet101 | 36.22 | 0.026 | 0.026 | 0.083 | 0.022 |
Swin-Tiny | 30.23 | 0.097 | 0.065 | 0.040 | 0.044 |
Swin-Base | 38.27 | 0.067 | 0.044 | 0.027 | 0.034 |
Disease level | ResNet50 | ResNet101 | Swin-Tiny | Swin-Base | ResNet50 | ResNet101 | Swin-Tiny | Swin-Base |
---|---|---|---|---|---|---|---|---|
Box average precision | Mask average precision | |||||||
1 | 100.00 a | 100.00 a | 80.00 | 100.00 a | 90.00 | 97.33 a | 60.00 | 80.00 |
2 | 98.88 a | 98.86 | 98.01 | 98.77 | 95.95 a | 95.42 | 90.52 | 93.07 |
3 | 97.88 | 98.16 | 98.21 a | 97.97 | 94.65 | 95.46 a | 91.13 | 93.96 |
4 | 98.99 | 99.00 a | 98.29 | 98.98 | 97.27 a | 96.73 | 91.45 | 93.98 |
5 | 100.00 a | 100.00 a | 100.00 a | 100.00 a | 100.00 a | 95.05 | 95.05 | 95.05 |
6 | 98.99 | 99.00 a | 98.42 | 99.00 a | 97.04 a | 96.80 | 91.47 | 93.91 |
7 | 98.80 a | 98.77 | 97.90 | 98.74 | 96.26 a | 95.59 | 90.17 | 92.95 |
8 | 97.97 a | 97.85 | 97.29 | 97.86 | 93.99 a | 93.71 | 89.31 | 91.08 |
9 | 98.87 | 98.89 | 98.25 | 98.98 a | 97.55 | 97.33 | 92.76 | 97.56 a |
Table 2. Box average precision and mask average precision of instance segmentation at different rice blast levels with RiceblastSegMask model using different backbone networks.
Disease level | ResNet50 | ResNet101 | Swin-Tiny | Swin-Base | ResNet50 | ResNet101 | Swin-Tiny | Swin-Base |
---|---|---|---|---|---|---|---|---|
Box average precision | Mask average precision | |||||||
1 | 100.00 a | 100.00 a | 80.00 | 100.00 a | 90.00 | 97.33 a | 60.00 | 80.00 |
2 | 98.88 a | 98.86 | 98.01 | 98.77 | 95.95 a | 95.42 | 90.52 | 93.07 |
3 | 97.88 | 98.16 | 98.21 a | 97.97 | 94.65 | 95.46 a | 91.13 | 93.96 |
4 | 98.99 | 99.00 a | 98.29 | 98.98 | 97.27 a | 96.73 | 91.45 | 93.98 |
5 | 100.00 a | 100.00 a | 100.00 a | 100.00 a | 100.00 a | 95.05 | 95.05 | 95.05 |
6 | 98.99 | 99.00 a | 98.42 | 99.00 a | 97.04 a | 96.80 | 91.47 | 93.91 |
7 | 98.80 a | 98.77 | 97.90 | 98.74 | 96.26 a | 95.59 | 90.17 | 92.95 |
8 | 97.97 a | 97.85 | 97.29 | 97.86 | 93.99 a | 93.71 | 89.31 | 91.08 |
9 | 98.87 | 98.89 | 98.25 | 98.98 a | 97.55 | 97.33 | 92.76 | 97.56 a |
Model | AP (IOU 0.50) | AP (IOU 0.75) | AP (IOU 0.90) | mAP (IOU 0.50‒0.95) |
---|---|---|---|---|
YOLACT | 92.09 | 90.50 | 48.78 | 77.12 |
YOLACT++ | 93.10 | 90.53 | 49.86 | 77.83 |
Fast R-CNN | 87.02 | 82.03 | 33.56 | 67.54 |
Mask R-CNN | 95.03 | 87.14 | 57.35 | 79.84 |
Transfiner | 95.08 | 85.33 | 58.33 | 79.58 |
RiceblastSegMask | 96.83 | 87.33 | 56.33 | 80.16 |
Table 3. Average precision of different target detection models. %
Model | AP (IOU 0.50) | AP (IOU 0.75) | AP (IOU 0.90) | mAP (IOU 0.50‒0.95) |
---|---|---|---|---|
YOLACT | 92.09 | 90.50 | 48.78 | 77.12 |
YOLACT++ | 93.10 | 90.53 | 49.86 | 77.83 |
Fast R-CNN | 87.02 | 82.03 | 33.56 | 67.54 |
Mask R-CNN | 95.03 | 87.14 | 57.35 | 79.84 |
Transfiner | 95.08 | 85.33 | 58.33 | 79.58 |
RiceblastSegMask | 96.83 | 87.33 | 56.33 | 80.16 |
Fig. 3. Representative unmanned aerial vehicle (UAV)-based images for rice blast resistance grading among different genotypes using RiceblastSegMask. The numbers in the figures represent the disease levels of rice blast.
Fig. 4. Instance segmentation of rice blast based on RiceblastSegMask (A) and confusion matrix of rice blast resistance assessment (B). The numbers in the figures represent the disease levels of rice blast. R, Resistant; M, Moderately resistant; S, Susceptible.
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