A YOLOv5-based target detection method using high-resolution remote sensing images

doi:10.6046/zrzyyg.2023052

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Abstract

High-resolution remote sensing images contain rich data and information, which reduce the difference between the target and the background, resulting in substandard detection accuracy and reduced target detection performance. Based on the deep learning algorithm You Only Look Once (YOLO), this study designed a lightweight network model GC-YOLOv5 by combining end-to-end coordinate attention (CA) and the lightweight network module GhostConv. The CA was employed to encode channels along the horizontal and vertical directions, enabling the attention mechanism module to simultaneously capture remote spatial interactions with precise location information and helping the network locate targets of interest more accurately. The original ordinary convolutional module convolutional-batchnormal-SiLu (CBS) was replaced by the GhostConv module, reducing the number of parameters in the feature channel fusion process and the size of the optimal model. Experiments were conducted on the GC-YOLOv5 using the publicly available NWPU-VHR-10 dataset, with the robustness of the model verified on the RSOD dataset. The results show that GC-YOLOv5 yielded a detection accuracy of 96.5% on the NWPU-VHR-10 dataset, with a recall rate of 96.4% and mAP of 97.7%. Moreover, GC-YOLOv5 achieved satisfactory results on the RSOD dataset.

Keywords deep learning remote sensing image target detection YOLOv5

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TP79

Issue Date: 14 June 2024

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	Shuangshuang SONG
	Kaifei XIAO
	Zhaohua LIU
	Zhaoliang ZENG

Cite this article:

Shuangshuang SONG,Kaifei XIAO,Zhaohua LIU, et al. A YOLOv5-based target detection method using high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 50-59.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023052 OR https://www.gtzyyg.com/EN/Y2024/V36/I2/50

Fig.1 Method flow chart

Fig.2 The improved network structure

Fig.3 CA operation structure

Fig.4 Normal convolution operations and GhostConv convolution module operations

Fig.5 Sample diagram of NWPU-VHR-10 dataset and RSOD dataset

Tab.1 Experimental environment configuration

Tab.2 Experimental training parameters

Fig.6 Results comparison chart of precision and mAP@0.5

Tab.3 Performance improvement of each part design on the result

Tab.4 Performance of different methods on NWPU-VHR-10 dataset

Fig.7 Example of detection results of NWPU-VHR-10 dataset

Tab.5 Comparison between the detection results of different algorithms and the real label in NWPU-VHR-10 dataset

模型	精度/%	召回率/%	mAP@0.5/%	FPS/(幅· $s - 1$ )
Faster-RCNN	91.8	89.8	--	--
YOLOv5	94.0	86.0	88.7	64.93
GC-YOLOv5	93.4	90.5	92.3	64.93

Tab.6 Performance of different methods on RSOD dataset

Fig.8-1 Example of detection results of RSOD dataset

Fig.8-2 Example of detection results of RSOD dataset

Tab.7 Comparison between the detection results of different algorithms and the real label in RSOD dataset

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