Remote-Sensing Image Detection Method Based on Contextual Awareness and Sparse Feature Fusion

Quan Feng; Liang Luo; Xiaoqian Zhang

doi:10.3788/LOP241744

Laser & Optoelectronics Progress, Volume. 62, Issue 6, 0628002(2025)

Remote-Sensing Image Detection Method Based on Contextual Awareness and Sparse Feature Fusion

Quan Feng^*, Liang Luo, and Xiaoqian Zhang

Author Affiliations

College of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan , China

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Abstract Get PDF(in Chinese)

Figures & Tables(9)

Fig. 1. Overall network structure

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Fig. 2. Context aware unit and MSDA Transformer. (a) Context aware unit; (b) MSDA Transformer

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Fig. 3. Sparse feature fusion strategy

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Fig. 4. Structures of GSConv, GSBottleneck and VoGSCSPC. (a) GSConv; (b) GSBottleneck; (c) VoGSCSPC

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Fig. 5. Detection results on NWPU VHR-10 and DIOR datasets

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Table 1. Experimental parameters
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Table 1. Experimental parameters
Experimental parameter Parameter quantity
Epoch 200
Batch size 16
Input size 640×640
Optimizer SGD
Momentum 0.937
Learning rate 0.01
Weight decay 0.0005

Table 2. Ablation results on NWPU VHR-10 dataset
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Table 2. Ablation results on NWPU VHR-10 dataset
No. CAU CFFS Slim-Neck mAP₅₀ /% AP_S /% AP_M /% AP_L /% FLOPs /10⁹ Params /10⁶
1 90.2 33.9 50.4 51.7 28.7 11.1
2 √ 91.1 35.3 53.2 54.6 31.1 11.3
3 √ √ 91.6 35.7 53.9 55.4 31.1 11.3
4 √ √ √ 91.8 36.1 53.0 55.8 27.8 10.4

Table 3. Comparative experimental results on the NWPU VHR-10 dataset

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Table 3. Comparative experimental results on the NWPU VHR-10 dataset

Method	mAP₅₀	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
SSD	80.3	84.6	71.5	85.3	88.4	89.3	71.2	99.4	86.4	64.9	62.6
Faster R-CNN	82.4	98.6	79.9	90.6	89.5	87.3	66.3	95.4	80.6	67.0	69.1
Mask R-CNN	83.9	85.2	82.3	88.1	90.0	90.1	75.2	97.6	89.6	67.6	73.6
TOOD	86.8	100.0	89.7	89.0	99.6	91.4	72.6	100.0	86.2	57.1	82.8
YOLOv5s	88.4	99.9	90.2	97.2	99.9	74.4	67.7	99.7	100.0	69.3	86.2
YOLOv8s	90.2	97.5	92.5	98.3	97.9	94.1	74.9	100.0	95.9	65.2	86.2
Proposed method	91.8	99.6	91.7	98.7	97.7	91.6	85.7	99.8	89.3	73.3	90.2

Table 4. Comparative experimental results on the DIOR dataset

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Table 4. Comparative experimental results on the DIOR dataset

Method	mAP₅₀	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
Method	mAP₅₀	C11	C12	C13	C14	C15	C16	C17	C18	C19	C20
SSD	58.4	59.5	72.7	72.4	75.7	48.1	65.8	56.6	63.3	53.0	65.3
SSD	58.4	68.6	49.4	28.2	59.2	61.2	46.6	76.3	55.1	27.4	64.7
Faster R-CNN	54.2	53.6	53.2	78.8	66.2	50.1	70.9	62.3	69.0	55.2	68.1
Faster R-CNN	54.2	56.9	50.2	28.3	27.7	72.0	39.8	75.2	38.6	23.6	45.4
Mask R-CNN	60.6	53.8	72.6	63.2	78.4	50.2	66.2	55.9	66.1	55.3	70.6
Mask R-CNN	60.6	69.2	44.3	30.6	68.0	55.3	50.6	81.1	56.2	45.6	78.3
TOOD	71.6	90.1	73.3	91.9	81.4	38.7	89.2	54.9	66.2	58.8	75.1
TOOD	71.6	75.2	54.4	61.6	75.8	86.1	80.7	87.5	50.8	68.5	72.9
YOLOv5s	74.4	92.9	73.7	93.2	83.1	48.7	88.6	56.9	71.3	70.3	69.5
YOLOv5s	74.4	79.4	66.9	63.9	94.4	91.6	81.3	90.9	55.2	75.8	80.3
YOLOv8s	76.9	93.5	73.1	94.7	83.5	48.0	89.6	64.2	72.2	70.5	75.8
YOLOv8s	76.9	76.1	69.8	65.4	94.0	96.1	82.3	92.5	50.5	74.3	82.8
Proposed method	79.5	93.6	81.2	94.2	82.3	50.9	90.9	67.7	74.4	68.4	80.2
Proposed method	79.5	82.8	72.6	67.3	94.0	94.5	82.6	92.8	57.5	76.0	82.9

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Quan Feng, Liang Luo, Xiaoqian Zhang. Remote-Sensing Image Detection Method Based on Contextual Awareness and Sparse Feature Fusion[J]. Laser & Optoelectronics Progress, 2025, 62(6): 0628002

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