Remote Sensing Rotating Object Detection Based on Multi-Scale Feature Extraction

Luobing Wu; Yuhai Gu; Wenhao Wu; Shuaixin Fan

doi:10.3788/LOP221716

Laser & Optoelectronics Progress, Volume. 60, Issue 12, 1228010(2023)

Remote Sensing Rotating Object Detection Based on Multi-Scale Feature Extraction

Luobing Wu¹, Yuhai Gu^1,2、*, Wenhao Wu¹, and Shuaixin Fan¹

¹Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100089, China

²Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100089, China

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Figures & Tables(12)

Fig. 1. Structure diagram of CenterNet model

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Fig. 2. Structure diagram of CenterNet-RS model

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Fig. 3. Schematic of principle and structure of receptive field expansion module. (a) Schematic of principle; (b) schematic of structure

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Fig. 4. Structure of adaptive feature fusion

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Fig. 5. Presentation of DOTA dataset

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Fig. 6. Effect presentations of the CenterNet-RS on DOTA dataset

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Fig. 7. Structure of models in the ablation experiment

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Table 1. Hardware and software environments for experimental and model construction
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Table 1. Hardware and software environments for experimental and model construction
Experiment Content
CPU Intel Core i5
GPU Nvidia RTX 2080 Ti
RAM 16 GB
OS Ubuntu 18.04
Plantform CUDA 10.0
Library PyTorch

Table 2. Comparison of performance of CenterNet-RS and other models on DOTA dataset

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Table 2. Comparison of performance of CenterNet-RS and other models on DOTA dataset

Model	Backbone	Rotatable-Head	mAP /%	Running time /s
R2CNN	VGG-16	√	60.67	0.263
ICN	ResNeXt101	√	68.16	0.221
CAD-Net	ResNet101	√	69.90	0.172
RoI-Trans	ResNet101	√	69.59	0.175
BBAVectors	ResNet101	√	75.36	0.086
CenterNet	ResNet101	×	63.56	0.072
RetinaNet	DLA-34	×	62.76	0.081
CenterNet-RS	ResNet101	√	73.01	0.078

Table 3. AP of different algorithms detecting various objects on DOTA dataset

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Table 3. AP of different algorithms detecting various objects on DOTA dataset

Category	R2CNN	ICN	CAD-Net	RoI-Trans	BBA-Vectors	CenterNet-RS
mAP	60.67	68.16	69.90	69.56	75.36	73.01
PL	80.89	81.36	87.80	88.64	88.63	92.37
BD	65.75	74.30	82.40	78.52	84.06	74.86
BR	35.34	47.70	49.40	43.44	52.13	50.42
GTF	67.44	70.32	73.50	75.92	69.56	66.43
SV	59.93	64.89	71.10	68.81	78.26	64.49
LV	50.91	67.82	63.50	73.68	80.40	79.63
SH	55.81	69.98	76.70	83.59	88.06	76.74
TC	90.67	90.76	90.90	90.74	90.87	93.96
BC	66.92	79.06	79.20	77.27	87.23	75.27
ST	72.39	78.20	73.30	81.46	86.39	87.30
SBF	55.06	53.64	48.40	58.39	56.11	62.72
RA	52.23	62.90	60.90	53.54	65.62	69.16
HA	55.14	67.02	62.00	62.83	67.10	66.76
SP	53.35	64.17	67.00	58.93	72.08	59.60
HC	48.22	50.23	62.20	47.67	63.96	56.05

Table 4. AP of different algorithms detecting various objects on UCAS-AOD dataset

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Table 4. AP of different algorithms detecting various objects on UCAS-AOD dataset

Method	Backbone	AP		mAP
Method	Backbone	Car	Airplane	mAP
R2CNN*	VGG-16	78.89	89.76	84.32
ICN	ResNeXt101	85.02	90.32	87.67
CAD-Net	ResNet101	88.35	89.93	89.14
RoI-Trans*	ResNet101	87.99	89.90	88.95
BBAVectors	ResNet101	90.27	91.41	90.84
RetinaNet *	DLA-34	83.64	89.51	86.57
CenterNet	ResNet101	79.90	90.61	85.25
CenterNet-RS	ResNet101	89.37	92.82	91.10

Table 5. Comparison of AP values of different methods in ablation experiment

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Table 5. Comparison of AP values of different methods in ablation experiment

Category	CenterNet	FPN	FPN+ RFEM	AFF	Rotatable-Head	FPN+Rotatable-Head	CenterNet-RS
mAP	63.43	66.44	68.98	67.64	67.03	69.14	73.01
PL	90.20	91.24	91.94	90.81	90.99	91.13	92.37
BD	68.13	71.12	73.44	72.75	69.77	71.86	74.86
BR	44.72	47.28	46.01	45.18	48.23	49.89	50.42
GTF	57.49	59.98	64.98	62.98	58.39	59.98	66.43
SV	54.44	54.69	56.47	56.44	63.81	63.16	64.49
LV	71.37	75.72	75.29	72.35	75.67	79.12	79.63
SH	66.39	68.54	69.93	68.80	73.96	75.21	76.74
TC	89.82	90.58	91.79	93.76	92.17	92.03	93.96
BC	63.22	66.90	71.55	70.86	69.96	72.74	75.27
ST	76.59	84.25	85.98	86.77	76.45	83.21	87.3
SBF	60.05	61.16	64.56	59.87	60.61	60.82	62.72
RA	62.82	65.48	67.63	65.43	64.95	66.71	69.16
HA	57.28	60.23	62.83	63.66	63.76	65.81	66.76
SP	47.97	51.26	57.63	55.00	50.92	53.31	59.60
HC	40.93	48.17	54.68	49.99	45.72	52.06	56.05

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Luobing Wu, Yuhai Gu, Wenhao Wu, Shuaixin Fan. Remote Sensing Rotating Object Detection Based on Multi-Scale Feature Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228010

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Paper Information

Category: Remote Sensing and Sensors

Received: May. 27, 2022

Accepted: Jul. 14, 2022

Published Online: Jun. 5, 2023

The Author Email: Yuhai Gu (guyuhai@bistu.edu.cn)

DOI:10.3788/LOP221716

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Hardware and software environments for experimental and model construction

Table 1. Hardware and software environments for experimental and model construction

Table 2. Comparison of performance of CenterNet-RS and other models on DOTA dataset

Table 2. Comparison of performance of CenterNet-RS and other models on DOTA dataset

Table 3. AP of different algorithms detecting various objects on DOTA dataset

Table 3. AP of different algorithms detecting various objects on DOTA dataset

Table 4. AP of different algorithms detecting various objects on UCAS-AOD dataset

Table 4. AP of different algorithms detecting various objects on UCAS-AOD dataset

Table 5. Comparison of AP values of different methods in ablation experiment

Table 5. Comparison of AP values of different methods in ablation experiment