跨尺度特征融合的遥感微小目标检测算法

doi:10.12305/j.issn.1001-506X.2025.05.05

Abstract

Abstract:

For three problems of shallow thinning features, deep semantic representation and multi-scale information extraction for the detection of small targets in remote sensing images, a cross-scale YOLOv7(CSYOLOv7) network by comprehensively applying multiple technologies is proposed. Firstly, a cross-stage feature extraction module (CFEM) and a receptive field feature enhancement module (RFFEM) are designed. CFEM is to improve the model's ability of refining feature extraction and suppress the loss of features during shallow down-sampling. RFFEM is to increase the network's ability of extracting deep semantic features and improve the model's ability of acquiring target context information. Secondly, a cross-gradient space pyramid pool module (CSPPM) is designed to effectively fuse global multi-scale and local features of small targets. Finally, shape intersection over union (Shape IoU) is used to replace the complete intersection over union (CIoU) to improve the accuracy of the model in the bounding box positioning task. Experimental results show that the CSYOLOv7 network achieves detection accuracy of 74% and 89.6% on the Dataset for Image Object Recognition (DIOR) data set and Northwestern Polytechnical University Very High Resolution-10 (NWPU VHR-10) data set respectively, which effectively improves the detection effect of small targets in remote sensing images.

Key words: remote sensing image, small target, feature extraction, context information

CLC Number:

TP751.2

Kai SHAO, Haogang LI, Yan LIANG, Jing NING, Wu CHEN. Remote sensing small target detection algorithm based on cross-scale feature fusion[J]. Systems Engineering and Electronics, 2025, 47(5): 1421-1431.

Figures/Tables 14

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算法	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13	C14	C15	C16	C17	C18	C19	C20	mAP/%
Faster-RCNN^[8]	12.3	20.5	15.2	61.6	48.8	70.8	40.2	79.1	24.3	55.4	47.7	42.7	43.1	65.2	53.9	84.4	51.9	48.2	72.7	84.8	51.1
Mask-RCNN^[9]	54.0	53.6	71.9	53.8	72.3	73.0	55.9	81.1	38.7	71.6	62.5	44.2	54.0	58.6	75.8	63.2	80.1	56.5	72.6	81.0	63.5
SSD^[11]	34.0	54.8	82.2	49.7	78.0	64.9	45.4	85.6	24.8	52.2	50.5	53.5	39.5	58.3	63.5	71.9	75.2	47.9	74.9	88.4	59.8
YOLOv5^[17]	54.2	76.5	89.1	73.7	81.6	71.9	51.7	86.9	42.1	62.5	59.6	59.5	49.8	72.9	71.6	78.5	77.7	56.9	76.2	88.1	69.1
YOLOX^[37]	55.8	70.8	86.9	69.1	88.3	70.1	53.7	88.5	41.4	62.5	61.3	59.0	53.4	71.4	72.1	72.0	76.7	56.3	72.6	88.2	68.5
RT-DETR^[38]	54.6	74.8	88.5	74.7	76.7	71.6	52.1	83.9	37.2	61.7	56.8	59.0	54.7	64.4	66.6	72.3	77.0	54.3	72.9	86.1	67.0
YOLOv8^[39]	53.5	77.8	91.0	78.1	82.6	77.3	60.9	88.9	42.3	64.9	59.2	64.3	53.9	72.4	73.1	79.8	78.8	59.0	77.7	88.6	71.2
YOLOv7^[26]	53.8	77.6	90.1	74.6	92.5	70.3	48.9	90.5	41.7	68.4	61.7	61.5	50.2	81.4	76.6	83.7	80.1	57.9	76.6	89.4	71.4
CSYOLOv7	58.0	78.4	90.5	77.2	93.4	78.2	58.8	90.6	44.9	74.4	65.2	63.2	53.1	83.8	77.8	84.4	80.2	59.6	77.7	90.1	74.0

算法	airplane	ship	S-T	B-D	T-C	B-C	G-T-F	harbor	bridge	vehicle	GFLOPs(×10⁹)	Params(×10⁶)	mAP/%
Faster-RCNN^[8]	92.5	67.2	38.7	97.1	60.5	16.4	92.4	67.9	70.5	33.4	67.5	41.4	63.7
Mask-RCNN^[9]	93.0	75.5	92.9	90.4	90.3	91.2	95.1	75.2	60.5	74.2	114	43.7	83.9
SSD^[11]	94.5	86.8	93.2	97.6	85.6	83.7	91.4	78.7	69.2	90.1	88.8	25.7	87.1
YOLOv5^[17]	99.0	84.4	86.2	98.3	76.2	69.2	90.6	83.3	58.8	86.1	15.9	7.1	83.2
YOLOX^[37]	99.6	79.8	90.6	90.9	74.7	74.2	99.0	69.3	75.7	82.7	26.7	8.9	83.7
RT-DETR^[38]	98.5	86.8	89.3	94.8	86.0	72.8	95.4	80.2	71.4	82.4	58.4	20.1	85.8
YOLOv8^[39]	99.2	87.6	83.2	98.8	76.3	79.6	98.4	89.0	65.2	82.4	28.4	11.2	86.0
YOLOv7^[26]	99.4	86.4	83.4	99.0	77.4	78.2	93.8	90.3	81.3	77.6	13.3	6.1	86.5
CSYOLOv7	99.5	87.8	98.6	98.7	87.6	82.4	96.4	88.1	75.8	80.7	19.1	7.4	89.6

算法	RFFEM	CFEM	CSPPM	Shape IoU	P/%	R/%	Params(×10⁶)	mAP0.5/%
YOLOv7	-	-	-	-	79.9	67.2	6.1	71.4
实验a	√	-	-	-	82.2	67.2	6.2	72.3
实验b	-	√	-	-	80.9	68.2	6.2	72.2
实验c	-	-	√	-	80.8	67.1	7.2	71.8
实验d	√	√	-	-	82.6	68.1	6.2	73.3
实验e	√	-	√	-	82.2	67.7	7.3	72.6
实验f	√	√	√	-	83.6	67.8	7.4	73.6
本文算法	√	√	√	√	83.1	68.3	7.4	74.0

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Remote sensing small target detection algorithm based on cross-scale feature fusion

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