Location of Typhoon Center Based on Multi-Scale Mosaic Mask R-CNN

Fig. 7. Loss function figures of Mask R-CNN model combined with data augmentation. (a) Proposed multi-scale mosaic; (b) Cutout; (c) CutMix; (d) mosaic

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Fig. 8. Deep learning method for locating typhoon center. (a) Faster R-CNN; (b) YOLOv3; (c) Mask R-CNN

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Fig. 9. Fitting diagrams of real coordinates and segmented coordinates of model. (a) HAISHEN; (b) VAMCO

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Table 1. Scale division of typhoon eye
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Table 1. Scale division of typhoon eye
Level of scale Radius size Number of pictures
Small $r$ < 1.43 1467
Middle 1.43 ≤ $r$ < 1.90 1467
Large $r$ ≥ 1.90 1466

Table 2. Universal hyperparameter settings
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Table 2. Universal hyperparameter settings
Hyperparameter Value
NMS-thresh 0.45
Score-thresh 0.5
Maximum iterations 40000
Model checkpoint 2
Learning rate 10^-4-10^-6
Weight decay 5×10^-4
GPU number 1
Input size 128×128

Table 3. Results of multi-scale experiments

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Table 3. Results of multi-scale experiments

Scale combination	Number	MSE	Mean MSE	MIoU	Mean MIoU	MPA	Mean MPA	FPS	Mean FPS
Master model	1	3802		0.9086		0.8959		20.4
Large+Middle	1	2154	2554	0.9454	0.9421	0.9263	0.9218	17.1	20.6
	2	2981		0.9384		0.9170		23.3
	3	2527		0.9426		0.9221		21.5
Large+Small	1	3290	3415	0.9322	0.9313	0.9098	0.9115	19.4	21.4
	2	3747		0.9270		0.9061		22.7
	3	3208		0.9348		0.9186		22.2
Middle+Small	1	3685	4176	0.9259	0.9151	0.8836	0.8896	20.8	20.7
	2	4761		0.9151		0.8908		19.8
	3	4082		0.9044		0.8944		21.5
Large+Middle+Small	1	3125	3355	0.9187	0.9222	0.8968	0.9043	22.1	20
	2	3588		0.9221		0.9047		18.7
	3	3351		0.9258		0.9113		19.2

Table 4. Comparison of wind eye segmentation results at different scales
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Table 4. Comparison of wind eye segmentation results at different scales
Model MIoU^S MIoU^M MIoU^L MPA^S MPA^M MPA^L
Mask R-CNN 0.8880 0.9145 0.9233 0.8830 0.8831 0.9216
Mask R-CNN+Proposed multi-scale mosaic 0.9305 0.9469 0.9489 0.9090 0.9197 0.9367

Table 5. Experimental results of each data augmentation
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Table 5. Experimental results of each data augmentation
Model Data augmentation method MSE MIoU MPA FPS
Mask R-CNN 3802 0.9086 0.8959 20.4
Proposed multi-scale mosaic 2554 0.9421 0.9218 20.6
Cutout 5373 0.8569 0.8247 22.8
CutMix 4006 0.9135 0.9061 17.0
mosaic 3249 0.9207 0.9074 15.4

Table 6. Comparison of location accuracy of typhoon center
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Table 6. Comparison of location accuracy of typhoon center
Model Mean error Mean variance
Longitude Latitude Longitude Latitude
Faster R-CNN 0.31 0.26 0.055 0.046
YOLOv3 0.28 0.25 0.043 0.039
Mask R-CNN+Proposed multi-scale mosaic 0.17 0.19 0.028 0.025

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Zongsheng Zheng, Jiahui Zhao, Peng Lu, Guoliang Zou, Zhenhua Wang. Location of Typhoon Center Based on Multi-Scale Mosaic Mask R-CNN[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010009

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