OrientedFormer: An End-to-End Transformer-Based Oriented Object Detector in Remote Sensing Images

Zeyu Ding; Yong Zhou; Jiaqi Zhao; Hancheng Zhu; Wen-Liang Du; Rui Yao; Abdulmotaleb El Saddik

cs.CVSep 29, 2024

OrientedFormer: An End-to-End Transformer-Based Oriented Object Detector in Remote Sensing Images

Authors: Zeyu Ding, Yong Zhou, Jiaqi Zhao, Hancheng Zhu, Wen-Liang Du, Rui Yao, Abdulmotaleb El Saddik

Organizations: School of Computer Science and Technology, China University of Mining and Technology, and with Mine Digitization Engineering Research Center of the Ministry of Education, and also with Innovation Research Center of Disaster Intelligent Prevention and Emergency Rescue, China University of Mining and Technology, Xuzhou 221116, China · School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada

Abstract

Oriented object detection in remote sensing images is a challenging task due to objects being distributed in multi-orientation. Recently, end-to-end transformer-based methods have achieved success by eliminating the need for post-processing operators compared to traditional CNN-based methods. However, directly extending transformers to oriented object detection presents three main issues: 1) objects rotate arbitrarily, necessitating the encoding of angles along with position and size; 2) the geometric relations of oriented objects are lacking in self-attention, due to the absence of interaction between content and positional queries; and 3) oriented objects cause misalignment, mainly between values and positional queries in cross-attention, making accurate classification and localization difficult. In this paper, we propose an end-to-end transformer-based oriented object detector, consisting of three dedicated modules to address these issues. First, Gaussian positional encoding is proposed to encode the angle, position, and size of oriented boxes using Gaussian distributions. Second, Wasserstein self-attention is proposed to introduce geometric relations and facilitate interaction between content and positional queries by utilizing Gaussian Wasserstein distance scores. Third, oriented cross-attention is proposed to align values and positional queries by rotating sampling points around the positional query according to their angles. Experiments on six datasets DIOR-R, a series of DOTA, HRSC2016 and ICDAR2015 show the effectiveness of our approach. Compared with previous end-to-end detectors, the OrientedFormer gains 1.16 and 1.21 AP $_{50}$ on DIOR-R and DOTA-v1.0 respectively, while reducing training epochs from 3 $\times$ to 1 $\times$ . The codes are available at https://github.com/wokaikaixinxin/ai4rs and https://github.com/wokaikaixinxin/OrientedFormer.

Figures & tables

Panel of Figure 1 — Fig. 1: (a) Object instances distribute in remote sensing images with arbitrary orientation. Angles are used to characterize oriented objects, in addition to positionsa and sizes. (b) Visualization of sampling points of the oriented cross-attention for alignment.

Notation	Description	Notation	Description
$(x,y,w,h,\theta)$	oriented boxes	$d_{q}$	dimension of self-attention
$(x,y,z,r,\theta)$	oriented boxes	$G$	Wasserstein distance score
$\left\{f^{l}\right\}^{L}_{l=1}$	levels of feature map	$\tau$ , $\epsilon$	coefficient of $G$
$H_{l}$	height of feature map	$(\Delta x,\Delta y,\Delta z)$	offset of sampling points
$W_{l}$	width of feature map	$g$	number of heads
$D$	channel of feature map	$O$	number of sampling points

TABLE I: Nomenclature with related notations.

Fig. 2: Overall architecture of the OrientedFormer. Features are extracted from images. An object query is decomposed into a content query $Q_{c}$ and a positional query $Q_{p}$ . The Gaussian PE encodes positional queries. The Wasserstein self-attention measures the geometric relations between two different content queries by utilizing Wasserstein distance scores. The oriented cross-attention is proposed to align values and positional queries.

Panel of Figure 4 — Fig. 3: An example of Gaussian positional encoding. (a) positional encoding in Deformable DETR. (b) Gaussian positional encoding.

Panel of Figure 5 — Fig. 4: Self-attention in the decoder. (a) vanilla self-attention. (b)Wasserstein self-attention.

Fig. 5: Oriented cross-attention. It attends to sparse sampling points $(\tilde{x},\tilde{y},\tilde{z})$ around the center of a positional query. Sampling points are rotated according to angles for alignment. Values $V$ are interpolated by sampling points and multi-scale features. We deploy attention mechanisms separately on each particular dimension of values, i.e., scale-aware, channel-aware, and spatial-aware.

Method	config	value
OrientedFormer	optimizer	AdamW
	base learning rate	5e-5
	weight decay	1e-6
	optimizer momentum	$\beta_{1},\beta_{2}$ =0.9, 0.999
	batch size	4
	GPUs	2

TABLE II: Experiment settings of OrientedFormer.

Mehtod	Backbone	APL	APO	BF	BC	BR	CH	DAM	ETS	ESA	GF	GTF	HA	OP	SH	STA	STO	TC	TS	VE	WM	AP 50
one-stage:
RetinaNet-O [ 33 ]	R50	61.49	28.52	73.57	81.17	23.98	72.54	19.94	72.39	58.20	69.25	79.54	32.14	44.87	77.71	67.57	61.09	81.46	47.33	38.01	60.24	57.55
DFDet [ 40 ]	R50	61.92	38.83	77.41	81.36	34.11	74.97	26.26	62.31	76.06	75.56	79.62	38.26	52.76	80.40	73.11	68.27	81.38	52.23	44.11	63.35	62.11
Oriented Rep [ 9 ]	R50	70.03	46.11	76.12	87.19	39.14	78.76	34.57	71.80	80.42	76.16	79.41	45.48	54.90	87.82	77.03	68.07	81.60	56.83	51.57	71.25	66.71
DCFL [ 7 ]	R50	68.60	53.10	76.70	87.10	42.10	78.60	34.50	71.50	80.80	79.70	79.50	47.30	57.40	85.20	64.60	66.40	81.50	58.90	50.90	70.90	66.80
two-stage:

TABLE III: Comparison with state-of-the-art methods on the DIOR-R . The results in bold denote the best performance of each column.

Method	Backbone	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC	AP 50
one-stage:
PSC [ 17 ]	R50	88.24	74.42	48.63	63.44	79.98	80.76	87.59	90.88	82.02	71.58	59.12	60.78	65.78	71.21	53.06	71.83
R3Det [ 15 ]	R101	88.76	83.09	50.91	67.27	76.23	80.39	86.72	90.78	84.68	83.24	61.98	61.35	66.91	70.63	53.94	73.79
S 2 A-Net [ 16 ]	R50	89.11	82.84	48.37	71.11	78.11	78.39	87.25	90.83	84.90	85.64	60.36	62.60	65.26	69.13	57.94	74.12
H2RBox [ 44 ]	R50	88.93	78.89	46.27	68.79	81.12	75.45	86.68	90.89	86.71	87.33	64.15	68.83	62.81	69.39	59.79	74.40
CFA [ 18 ]	R50	88.34	83.09	51.92	72.23	79.95	78.68	87.25	90.90	85.38	85.71	59.63	63.05	73.33	70.36	47.86	74.51

TABLE IV: Comparison with state-of-the-art methods on the DOTA-v1.0 dataset. * denotes multi-scale trainig and testing. The results in bold denote the best performance of each column.

Fig. 6: P-R curve (IoU=0.5) on the DIOR-R test set with ResNet50.

Method	SV	LV	SH	ST	SP	CC	AP 50
RetinaNet-O [ 33 ]	44.53	56.79	73.31	59.96	64.52	0.83	59.16
Faster RCNN-O [ 53 ]	51.28	68.98	79.37	67.50	65.28	1.54	62.00
Mask R-CNN [ 54 ]	51.31	71.34	79.75	66.07	64.46	9.42	62.67
HTC [ 55 ]	51.54	73.31	80.31	67.34	64.48	5.15	63.40
ReDet [ 5 ]	52.38	75.73	80.92	68.64	70.55	11.53	66.86
OrientedFormer	64.05	77.04	85.33	78.11	72.08	10.86	67.06

TABLE V: Main results of small size objects on DOTA-v1.5 . The results in bold denote the best performance of each column.

Method	Precision	Recall	F-measure	FLOPs
SASM [ 8 ]	56.7	77.9	65.7	71G
PSC [ 17 ]	83.7	63.2	72.0	78G
Retinanet-O [ 33 ]	83.9	67.5	74.8	77G
GWD [ 56 ]	84.4	67.6	75.1	77G
R3DET [ 15 ]	83.2	69.2	75.6	120G
Oriented RCNN [ 4 ]	73.9	80.9	77.2	100G

TABLE VI: Main results of precision, recall (IoU=0.5), F-measure and Flops on ICDAR2015 . The results in bold denote the best performance. The backbone used by all methods is Resnet50.

Fig. 7: P-R curve (IoU=0.75) on the DIOR-R test set with ResNet50.

Methods	Backbone	mAP(07)	mAP(12)
PSC [ 17 ]	R50	85.65	-
RoI Transformer [ 41 ]	R101	86.20	-
Gliding Vertex [ 6 ]	R101	88.20	-
PIoU [ 57 ]	DLA34	89.20	-
CenterMap [ 58 ]	R50	-	92.8
R3Det [ 15 ]	R101	89.26	96.01

TABLE VII: Comparison with the state-of-the-art methods on the HRSC2016 dataset. 07 means evaluation under pascal voc2007 metric, and 12 means evaluation under pascal voc2012 metric.

Method	SASM [ 8 ]	RetinaNet-O [ 33 ]	Oriented Rep [ 9 ]	Mask R-CNN [ 54 ]
AP 50	44.53	46.68	48.95	49.47
Method	ATSS-O [ 61 ]	S 2 A-Net [ 16 ]	HTC [ 55 ]	DCFL [ 7 ]
AP 50	49.57	49.86	50.34	51.57
Method	RoI Trans. [ 41 ]	S 2 A-Net $+$ DCFL	Oriented R-CNN [ 4 ]	OrientedFormer
AP 50	52.81	52.84	53.28	54.27

TABLE VIII: Performance comparisons on the DOTA-v2.0 dataset.

TABLE IX: OrientedFormer ablation experiments with ResNet-50 on DIOR-R. Default choice for our model is colored gray

PE	-	Deform.	DAB.	Learnable	Gaussian
AP 50	66.85	65.97	66.03	64.27	67.28

TABLE X: Comparsions of different positional encoding in the decoder.

Methods	Gaussian PE	Wasserstein Self-Attention	Oriented Cross-Attention	DIOR-R
Methods	Gaussian PE	Wasserstein Self-Attention	Oriented Cross-Attention	AP 50	AP 75	AP 50:95
Oriented Former				62.69	44.18	41.38
	✓	✓		63.08	43.44	41.00
			✓	65.78	43.69	41.87
		✓	✓	66.85	46.22	43.73
	✓		✓	67.03	44.07	42.49

TABLE XI: The effectiveness of proposed individual modules on DIOR-R.

Self-Attention	-	iof	iou	Wasserstein
AP 50	67.03	66.57	67.08	67.28

TABLE XII: Comparsions of different self-attention in the decoder.

Methods	Gaussian PE	Wasserstein Self-Attention	Oriented Cross-Attention	DOTA-v1.0
Methods	Gaussian PE	Wasserstein Self-Attention	Oriented Cross-Attention	AP 50	AP 75	AP 50:95
Oriented Former				73.81	47.74	45.40
	✓	✓		74.55	49.26	46.28
			✓	74.64	47.80	45.85
		✓	✓	74.69	46.16	45.12
	✓		✓	74.76	48.95	45.97

TABLE XIII: The effectiveness of individual modules on DOTA-v1.0.

Fig. 8: Convergence curves of Deformable DETR-O with CLS, ARS-DETR, OrientedFormer with ResNet50, Swin-T and LSK-T on DIOR-R.

Fig. 9: Epochs of training stage versus accuracy on the DOTA-v1.0 test set.

Method	Frame	Backbone	FPS	Params	FLOPs	AP 50
RoI Transformer [ 41 ]	Two- stage	R50	9.2	55M	253G	74.61
Oriented RCNN [ 4 ]		R50	7.3	41M	225G	75.87
Gliding Vertex [ 6 ]		R101	10.2	41M	225G	75.02
R3Det [ 15 ]	One- stage	R101	6.1	42M	335G	73.79
CFA [ 18 ]		R50	16.6	37M	194G	74.51
SASM [ 8 ]		R50	15.8	37M	194G	74.92

TABLE XIV: Speed, Parameters, FLOPs and accuracy on DOTA-v1.0.

Method	Backbone	Layers	AP 50	AP 75	AP 50:95	Params	FLOPs
Oriented -Former	R50	1	56.21	33.54	33.23	41M	287G
		2	60.69	39.86	38.35	41M	297G
		3	66.37	44.14	42.35	42M	315G
		4	67.28	44.13	42.66	44M	325G

TABLE XV: Comparsions of different layers of Backbone on DIOR-R.

	Methods	AP 50	AP 75
OrientedFormer	Fixed Offsets	65.98	43.02
	Deformable Offsets	66.66	44.10
	Random Offsets	66.70	43.47
	Oriented Cross-attention	67.28	44.13

TABLE XVI: Comparison of Different Sampling Methods on DIOR-R.

Panel of Figure 26 — Fig. 10: Comparison between our method and others on challenging samples. Confidence threshold is set to 0.3. Blue circles denote error results. Yellow circles denote missed results. The 1st line: Large-scale objects, e.g., soccer fields and ground track fields. The 2nd line: Densely packed objects, e.g., planes. The 3rd line: Images with complex backgrounds, e.g., the disturbance of letters. The 4th line: Images under poor environmental conditions.

Fig. 11: Sampling points in cross-attention. Oriented cross-attention (left column) rotates sampling points by angles for alignment. Deformable offsets method (middle column) does not rotate sampling points. Random offsets method (right column) employs random sampling points.

Fig. 12: Center points of learned positonal queries.

Fig. 13: Visualization of oriented cross-attention. For readibility, we draw the center of positional queries and sampling points.

Fig. 14: Visualization results of our method for DOTA, DIOR, and HRSC2016. DOTA contains images depicting extreme weather and poor lighting conditions. Oriented boxes, labels, and confidences are drawn.

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