A YOLOv5-based target detection method using high-resolution remote sensing images
SONG Shuangshuang1(), XIAO Kaifei1, LIU Zhaohua1(), ZENG Zhaoliang2
1. School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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.
Fig.4 Normal convolution operations and GhostConv convolution module operations
Fig.5 Sample diagram of NWPU-VHR-10 dataset and RSOD dataset
平台
配置
脚本语言
Python3.7.15
深度学习框架
Torch1.12.1+cu113
GPU类型
Tesla T4
NVIDIA
NVIDIA-SMI 460.32.03
CUDA版本
CUDA Version:11.2
Tab.1 Experimental environment configuration
参数
配置
神经网络优化器
SGD
批次大小
16
学习率
0.01
动量参数
0.937
权重衰减
0.000 5
训练轮数
200
Tab.2 Experimental training parameters
Fig.6 Results comparison chart of precision and mAP@0.5
模型
精度/%
召回率/%
mAP@0.5/%
参数数量/106个
权重文件数量/MB
FPS/(幅·s-1)
YOLOv5
93.1
92.5
95
12.34
24.6
40.65
YOLOv5-CA
92.5
95.6
95.6
12.36
25.9
50.76
YOLOv5-Ghost
93.4
91.7
93.7
9.67
19.5
47.62
GC-YOLOv5
96.5
96.4
97.7
11.37
20.5
46.51
Tab.3 Performance improvement of each part design on the result
模型
精度/ %
召回 率/%
mAP@0.5/%
FPS/(幅·s-1)
Faster-RCNN
91.8
93.8
—
—
YOLOv5
93.1
92.5
95
40.65
GC-YOLOv5
96.5
96.4
97.7
46.51
Tab.4 Performance of different methods on NWPU-VHR-10 dataset
Fig.7 Example of detection results of NWPU-VHR-10 dataset
图像类别
真实标签
Faster-RCNN
YOLOv5
GC-YOLOv5
飞机
13
13
13
13
网球场
4
4
4
4
操场
1
1
1
1
汽车
0
0
0
1
棒球场
0
1
0
0
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·)
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
图像类别
真实标签
Faster-RCNN
YOLOv5
GC-YOLOv5
飞机
10
10
10
10
立交桥
1
2
1
1
Tab.7 Comparison between the detection results of different algorithms and the real label in RSOD dataset
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2024,
Vol. 36
), XIAO Kaifei1, LIU Zhaohua1(
