Sparse Transformer Based Remote Sensing Rotated Object Detection

Fig. 2. Schematic diagrams of multiple self attention. (a) Neighborhood related self attention; (b) cross step self attention; (c) sparse self attention

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Fig. 3. Geometric representation of rotated bounding box

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Fig. 4. Detection results of proposed model on different datasets. (a) DOTA; (b) UCAS-AOD

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Fig. 5. Visualization of test results of different models. (a) RoI-Trans model; (b) $O^{2}$ DETR model; (c) proposed model

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Table 1. AP values of different models on DOTA dataset

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Table 1. AP values of different models on DOTA dataset

Model	R2CNN^［12］	RRPN^［13］	CAD-Net^［14］	RoI-Trans^［15］	$O^{2}$ DETR^［8］	Proposed model
Pl	80.89	88.52	87.80	88.64	86.01	89.91
BD	65.75	71.20	82.40	78.52	75.92	85.78
BR	35.34	31.66	49.40	43.44	46.02	50.65
GTF	67.44	59.30	73.50	75.92	66.65	78.16
SV	59.93	51.85	71.10	68.81	79.70	64.34
LV	50.91	56.19	63.50	73.68	79.93	75.43
SH	55.81	57.25	76.70	83.59	89.17	75.78
TC	90.67	90.81	90.90	90.74	90.44	90.88
BC	66.92	72.84	79.20	77.27	81.19	78.67
ST	72.39	67.38	73.30	81.46	76.00	84.45
SBF	55.06	56.69	48.40	58.39	56.91	57.91
RA	52.23	52.84	60.90	53.54	62.45	63.56
HA	55.14	53.08	62.00	62.83	64.22	64.56
SP	53.35	51.94	67.00	58.93	65.80	66.74
HC	48.22	53.58	62.20	47.67	58.96	66.33

Table 2. mAP values and detection frame rate of different detection algorithms on DOTA dataset

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Table 2. mAP values and detection frame rate of different detection algorithms on DOTA dataset

Model	mAP /%	Backbone	Frame rate /（frames·s^-1）
RRPN^［13］	61.01	VGG-16	5.25
R2CNN^［12］	60.67	VGG-16	3.81
CAD-Net^［14］	69.90	ResNet101	5.82
RoI-Trans^［15］	69.56	ResNet101	5.76
$O^{2}$ DETR^［8］	72.15	ResNet101	6.58
Proposed model	72.87	ResNet101	6.78

Table 3. AP values of different models on UCAS-AOD dataset
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Table 3. AP values of different models on UCAS-AOD dataset
Model Plane Car mAP
RRPN^［13］ 88.04 74.36 81.20
R2CNN^［12］ 89.76 78.89 84.32
R-DFPN^［16］ 88.91 81.27 85.09
P-RSDet^［11］ 92.69 87.38 90.03
Proposed model 91.22 89.58 90.40

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Linyuan He, Junqiang Bai, Xu He, Chen Wang, Xulun Liu. Sparse Transformer Based Remote Sensing Rotated Object Detection[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810003

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