Remote Sensing Image Target Detection Model Based on Attention and Feature Fusion

Branch	Layer name	Output size	Operation of convolution
Detection branch	conv4_3	64×64×512	$[3 \times 3 \times 1024]$
	fc6	32×32×1024	$[1 \times 1 \times 1024]$
	fc7	32×32×1024	$[\begin{array}{l} 1 \times 1 \times 256 \\ 3 \times 3 \times 512 \end{array}]$
	conv8_2	16×16×512	$[\begin{array}{l} 1 \times 1 \times 128 \\ 3 \times 3 \times 256 \end{array}]$
	conv9_2	8×8×256	$[\begin{array}{l} 1 \times 1 \times 128 \\ 3 \times 3 \times 256 \end{array}]$
	conv10_2	4×4×256	$[\begin{array}{l} 1 \times 1 \times 128 \\ 3 \times 3 \times 256 \end{array}]$
	conv11_2	2×2×256	$[\begin{array}{l} 1 \times 1 \times 128 \\ 3 \times 3 \times 256 \\ 3 \times 3 \times 256 \end{array}]$
	conv12_2	1×1×256	--
Attention branch	att_conv4	8×8×256	$[\begin{array}{l} 1 \times 1 \times 128 \\ 3 \times 3 \times 2 \end{array}]$
Attention branch	att_conv5	8×8×2	--

Table 2. Vehicle dimension in the UCAS-AOD data set
View table
Table 2. Vehicle dimension in the UCAS-AOD data set
Scale /pixel Scale1 (<100) Scale2 (100--200) Scale3 (200--300) Scale4 (>300) Total
Number(vehicle) 3704 1986 967 457 7114
Number(aircraft) 1028 1438 2593 2369 7482

Table 3. Detection results of different methods in UCAS-AOD data set
View table
Table 3. Detection results of different methods in UCAS-AOD data set
Method AP of plane /% AP of small-vehicle /% mAP /% S /s
SSD 88.13 85.09 86.61 0.36
Ref.[17] 90.66 88.17 89.41 0.34
AFFSSD 93.70 91.34 92.52 0.26

Table 4. Detection results of the two models on different scale targets
View table
Table 4. Detection results of the two models on different scale targets
Method SSD AFFSSD
AP of scale1/% 57.11 62.07
AP of scale2/% 64.09 70.31
AP of scale3/% 69.02 72.65
AP of scale4/% 69.71 71.33
S /s 38.90 38.10

Table 5. Detection results of different models in the NWPU VHR-10 data set unit: %

View table

Table 5. Detection results of different models in the NWPU VHR-10 data set unit: %

Model	RICNN^[18]	SSD	DSSD^[19]	Ref.[20]	Deformable R-FCN^[21]	Faster R-CNN^[22]	AFFSSD
Aircraft	88.35	84.32	86.50	95.20	87.30	94.60	87.02
Ship	77.34	62.90	65.40	79.70	81.40	82.30	83.50
Oil tank	85.27	78.25	90.30	73.70	63.60	65.32	80.69
Baseball diamond	88.12	89.33	89.60	96.40	90.40	95.50	96.02
Tennis court	40.83	79.41	85.10	71.60	81.60	81.90	80.32
Basketball court	58.45	87.69	80.40	72.10	74.10	89.70	90.10
Ground track field	86.73	80.61	78.20	99.70	90.30	92.40	81.36
Harbor	68.60	71.37	70.50	73.20	75.30	72.40	75.80
Bridge	61.51	65.35	68.20	57.00	71.40	57.50	72.03
Vehicle	71.10	62.30	74.20	72.00	75.50	77.80	78.01
mAP	72.63	76.15	78.84	79.06	79.09	80.94	82.49

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Yani Wang, Xili Wang. Remote Sensing Image Target Detection Model Based on Attention and Feature Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0228003

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