A multi-target semantic segmentation method for millimetre wave SAR images based on a dual-branch multi-scale fusion network

Fig. 6. Test results of each model. Each row represents the test results of the same picture, and each column represents the test results of the same model. Black denotes the background, green denotes the wrench, yellow denotes the pistol, red denotes the hammer, and blue denotes the knife

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Fig. 7. Baseline model

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Table 1. Architectures of DBFEN

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Table 1. Architectures of DBFEN

Stage	Output	DBFEN	Stage	Output	DBFEN
Conv1	256×256	3×3, 64, stride 2	Conv6	64×64	$(\begin{array}{l} 3 \times 3,128 \\ 3 \times 3,128 \end{array}) \times 2$
Conv2	128×128	3×3, 64, stride 2	Conv7	16×16	$(\begin{array}{l} 3 \times 3,256 \\ 3 \times 3,512 \end{array}) \times 2$
Conv3	64×64	$(\begin{array}{l} 3 \times 3,64 \\ 3 \times 3,128 \end{array}) \times 2$	Conv8	64×64	$(\begin{array}{l} 3 \times 3,128 \\ 3 \times 3,256 \end{array}) \times 2$
Conv4	64×64	$(\begin{array}{l} 3 \times 3,128 \\ 3 \times 3,128 \end{array}) \times 2$	Conv9	8×8	$(\begin{array}{l} 3 \times 3,512 \\ 3 \times 3,1024 \end{array}) \times 2$
Conv5	32×32	$(\begin{array}{l} 3 \times 3,128 \\ 3 \times 3,256 \end{array}) \times 2$

Table 2. Comparisons of the segmentation performance of each model in the HM-SAR dataset
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Table 2. Comparisons of the segmentation performance of each model in the HM-SAR dataset
Network model MPA/% mIoU/% F1/% Network model MPA/% mIoU/% F1/%
U-net 80.29 70.35 81.87 Deeplabv3+ 81.05 70.58 82.00
Pspnet 82.98 72.32 83.28 HRnet-v2 82.33 72.90 83.69
FCN-8s 81.29 72.11 83.11 DBMFnet (ours) 85.01 75.44 85.21

Table 3. Comparisons of the objects segmentation performance of each model in the HM-SAR dataset

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Table 3. Comparisons of the objects segmentation performance of each model in the HM-SAR dataset

Class	U-net		Pspnet		Deeplabv3+		HRnet-v2		FCN-8s		DBMFnet (ours)
Class	Pre	IoU	Pre	IoU	Pre	IoU	Pre	IoU	Pre	IoU	Pre	IoU
Hammer	80.74	61.98	76.49	63.7	80.15	63.99	79.93	67.35	79.16	65.17	81.91	69.33
Wrench	82.66	66.78	82.88	71.84	80.61	66.57	78.80	66.15	84.04	69.56	84.22	75.24
Pistol	75.63	63.77	77.3	64.21	75.45	62.65	85.71	69.47	81.07	65.81	87.89	70.56
Knife	78.59	59.4	81.36	62.01	78.82	59.84	81.67	61.68	80.06	60.16	82.55	66.15

Table 4. Calculation complexity and inference speed of each model
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Table 4. Calculation complexity and inference speed of each model
Network model Params/M GFLOPs Speed/(f/s)
U-net 24.89 452.31 32
Pspnet 46.7 118.43 33.5
FCN-8s 32.95 277.74 16
Deeplabv3+ 54.71 166.87 21
HRnet 29.55 80.18 11.5
DBMFnet(our) 19.54 47.36 26

Table 5. Comparisons of models using different decoder modules
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Table 5. Comparisons of models using different decoder modules
Network model mIoU Params/M GFLOPs
Baseline 72.61 23.15 38.78
Deeplabv3+(FCM) 70.58 54.71 166.87
FCN-8s(FDM) 72.11 32.95 277.74
Baseline+FCM 74.1 22.44 100.8
Baseline+FDM 73.16 21.65 45.27
Baseline+MSFM 75.44 23.06 47.86

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Junhua Ding, Minghui Yuan. A multi-target semantic segmentation method for millimetre wave SAR images based on a dual-branch multi-scale fusion network[J]. Opto-Electronic Engineering, 2023, 50(12): 230242-1

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