Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (7): 1384-1400.doi: 10.11947/j.AGCS.2024.20230455
• Photogrammetry and Remote Sensing • Previous Articles Next Articles
Jun YANG1,2(), Hengjing XIE1, Hongchao FAN3, Haowen YAN1
Received:
2023-10-07
Published:
2024-08-12
About author:
YANG Jun (1973—), male, PhD, professor, PhD supervisor, majors in 3D model spatial analysis, intelligent interpretation of remotely sensed big data, deep learning. E-mail: yangj@mail.lzjtu.cn
Supported by:
CLC Number:
Jun YANG, Hengjing XIE, Hongchao FAN, Haowen YAN. Multi-scale entropy neural architecture search for object detection in remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1384-1400.
Comparison of results before and after data preprocessing"
不同数据集的预处理 | 浮点运算数 | 参数量 | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|
未经过数据预处理(RSOD) | 207.63×109 | 30.29×106 | 92.4 | 99.2 | 98.5 | 67.6 | 89.8 | 94.7 |
经过数据预处理(RSOD) | 207.63×109 | 30.29×106 | 93.1 | 99.2 | 98.7 | 68.6 | 89.9 | 95.4 |
未经过数据预处理(DIOR) | 144.41×109 | 30.04×106 | 72.1 | 90.8 | 80.7 | 38.5 | 68.0 | 84.8 |
经过数据预处理(DIOR) | 144.41×109 | 30.04×106 | 75.5 | 92.6 | 84.4 | 44.3 | 72.1 | 87.0 |
未经过数据预处理(DOTA) | 208.15×109 | 30.31×106 | 70.8 | 87.5 | 81.6 | 50.0 | 76.6 | 82.5 |
经过数据预处理(DOTA) | 208.15×109 | 30.31×106 | 73.6 | 89.2 | 83.9 | 53.4 | 80.5 | 85.0 |
Comparison of different network models on the RSOD dataset"
网络模型 | 浮点运算数 | 参数量 | 网络搜索时间/h | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|---|
Faster R-CNN | 841.40×109 | 41.14×106 | — | 84.9 | 99.5 | 96.4 | 61.0 | 83.0 | 88.0 |
ResNet-SB | 298.57×109 | 46.89×106 | — | 77.4 | 97.5 | 90.5 | 23.3 | 66.4 | 82.3 |
VarifocalNet | 218.57×109 | 32.49×106 | — | 87.8 | 99.1 | 96.9 | 56.0 | 82.9 | 91.3 |
NAS-FCOS | 216.31×109 | 38.67×106 | 11.4 | 77.3 | 98.0 | 89.2 | 26.9 | 75.0 | 83.1 |
DetNAS | 201.67×109 | 32.79×106 | 12.6 | 85.5 | 99.5 | 97.9 | 63.2 | 83.2 | 88.6 |
本文方法 | 207.63×109 | 30.29×106 | 8.1 | 93.1 | 99.2 | 98.7 | 68.6 | 89.9 | 95.4 |
Comparison of different network models on the DIOR dataset"
网络模型 | 浮点运算数 | 参数量 | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|
Faster R-CNN | 798.04×109 | 32.95×106 | 66.1 | 88.8 | 74.2 | 12.2 | 50.8 | 76.2 |
ResNet-SB | 216.40×109 | 41.22×106 | 52.0 | 76.4 | 57.5 | 9.7 | 31.2 | 62.1 |
VarifocalNet | 159.35×109 | 32.49×106 | 70.1 | 89.6 | 78.3 | 35.2 | 64.6 | 83.0 |
NAS-FCOS | 230.62×109 | 38.15×106 | 55.7 | 80.4 | 60.6 | 10.4 | 35.8 | 65.5 |
DetNAS | 208.47×109 | 25.38×106 | 67.8 | 91.3 | 78.3 | 36.1 | 60.6 | 74.4 |
本文方法 | 144.41×109 | 30.04×106 | 75.5 | 92.6 | 84.4 | 44.3 | 72.1 | 87.0 |
Comparison of detection accuracy for different object categories on the DIOR dataset"
类别 | Faster R-CNN | ResNet-SB | VarifocalNet | NAS-FCOS | DetNAS | 本文方法 |
---|---|---|---|---|---|---|
飞机 | 81.5 | 67.8 | 72.7 | 75.5 | 80.6 | 81.3 |
机场 | 66.0 | 42.2 | 79.9 | 52.2 | 58.2 | 81.6 |
棒球场 | 86.5 | 77.6 | 83.2 | 83.5 | 85.4 | 86.0 |
篮球场 | 81.0 | 63.9 | 85.5 | 68.7 | 81.9 | 90.4 |
桥梁 | 43.7 | 31.8 | 48.8 | 27.5 | 55.3 | 60.0 |
烟囱 | 81.7 | 74.7 | 84.0 | 80.0 | 82.0 | 83.6 |
水坝 | 55.0 | 36.5 | 73.3 | 35.4 | 58.5 | 77.1 |
高速公路服务区 | 70.1 | 44.0 | 80.4 | 53.1 | 68.7 | 85.6 |
高速公路收费站 | 74.3 | 63.3 | 76.0 | 57.4 | 76.4 | 81.0 |
高尔夫球场 | 61.8 | 43.9 | 80.9 | 56.3 | 62.2 | 86.8 |
田径场 | 82.2 | 65.6 | 81.6 | 65.5 | 81.4 | 86.7 |
港口 | 41.7 | 26.3 | 41.7 | 34.0 | 51.4 | 52.4 |
立交桥 | 57.0 | 44.3 | 61.1 | 38.9 | 61.9 | 68.2 |
船舶 | 47.0 | 43.1 | 49.8 | 48.6 | 48.7 | 54.5 |
体育场 | 77.9 | 69.2 | 86.4 | 67.9 | 81.2 | 89.0 |
储油罐 | 67.3 | 62.5 | 58.7 | 71.3 | 71.0 | 64.6 |
网球场 | 90.4 | 79.9 | 86.0 | 82.0 | 89.5 | 89.1 |
火车站 | 49.2 | 19.2 | 71.1 | 25.4 | 52.1 | 76.2 |
车辆 | 46.7 | 42.0 | 43.4 | 47.0 | 53.0 | 53.2 |
风车 | 56.1 | 43.2 | 61.2 | 43.3 | 56.5 | 66.4 |
Comparison of different network models on the DOTA dataset"
网络模型 | 浮点运算数 | 参数量 | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|
Faster R-CNN | 264.11×109 | 41.53×106 | 57.3 | 81.6 | 66.3 | 35.5 | 62.3 | 68.7 |
ResNet-SB | 263.78×109 | 41.19×106 | 61.2 | 85.3 | 72.1 | 38.5 | 66.5 | 75.0 |
VarifocalNet | 245.69×109 | 32.52×106 | 66.1 | 86.7 | 77.1 | 45.5 | 70.0 | 79.0 |
NAS-FCOS | 249.36×109 | 38.69×106 | 54.5 | 80.6 | 62.2 | 27.3 | 58.8 | 69.0 |
DetNAS | 270.02×109 | 29.20×106 | 58.4 | 84.0 | 68.5 | 42.6 | 62.2 | 65.9 |
本文方法 | 208.15×109 | 30.31×106 | 73.6 | 89.2 | 83.9 | 53.4 | 80.5 | 85.0 |
Tab.8
Comparison of detection accuracy for different object categories on the DOTA dataset"
类别 | FasterR-CNN | ResNet-SB | VarifocalNet | FCOSNAS- | DetNAS | 本文方法 |
---|---|---|---|---|---|---|
飞机 | 74.5 | 74.9 | 81.0 | 73.8 | 75.1 | 83.7 |
船舶 | 43.8 | 43.2 | 56.6 | 42.5 | 53.5 | 58.0 |
储油罐 | 46.3 | 47.5 | 60.6 | 46.6 | 52.6 | 60.5 |
棒球场 | 59.4 | 65.4 | 65.9 | 63.0 | 55.9 | 78.2 |
网球场 | 87.9 | 88.3 | 93.1 | 85.3 | 88.8 | 94.5 |
篮球场 | 76.5 | 77.2 | 79.8 | 71.7 | 71.0 | 89.9 |
操场 | 64.0 | 70.0 | 71.6 | 36.8 | 59.9 | 83.3 |
港口 | 59.3 | 62.9 | 67.7 | 56.5 | 61.3 | 74.5 |
桥梁 | 43.8 | 50.5 | 56.6 | 39.6 | 54.8 | 77.3 |
大型车辆 | 63.6 | 64.7 | 73.7 | 61.8 | 66.7 | 76.4 |
小型车辆 | 37.1 | 38.8 | 47.9 | 36.4 | 42.2 | 52.3 |
直升机 | 53.8 | 67.6 | 67.0 | 58.4 | 62.0 | 77.1 |
环形交叉路口 | 54.1 | 58.6 | 65.3 | 54.7 | 51.0 | 79.1 |
足球场 | 58.7 | 66.7 | 47.1 | 51.8 | 47.0 | 60.3 |
游泳池 | 37.6 | 42.4 | 48.4 | 38.8 | 34.4 | 55.3 |
Comparison of backbone networks with different weight ratios in the low-level section"
α1:α2:α3:α4:α5 | 浮点运算数 | 参数量 | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|
1∶1∶1∶1∶1 | 894.54×109 | 31.25×106 | 88.3 | 98.7 | 97.3 | 61.2 | 84.7 | 89.7 |
0∶1∶1∶1∶1 | 476.23×109 | 30.26×106 | 90.9 | 99.2 | 97.8 | 65.4 | 85.3 | 91.5 |
0∶0∶1∶1∶1 | 206.28×109 | 28.60×106 | 91.8 | 99.2 | 98.1 | 65.0 | 88.0 | 94.0 |
0∶0∶0∶1∶1 | 131.77×109 | 28.17×106 | 89.5 | 98.9 | 97.7 | 60.8 | 86.5 | 92.9 |
Comparison of backbone network scales under different weight ratios"
α3:α4:α5 | 浮点运算数 | 参数量 | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|
1∶1∶1 | 206.28×109 | 28.60×106 | 91.3 | 99.2 | 98.1 | 65.0 | 88.0 | 94.0 |
1∶1∶2 | 206.31×109 | 29.84×106 | 92.1 | 99.2 | 98.5 | 67.7 | 87.7 | 94.5 |
1∶1∶3 | 207.92×109 | 29.93×106 | 92.8 | 99.2 | 98.6 | 68.5 | 9.5 | 95.2 |
1∶1∶4 | 207.63×109 | 30.29×106 | 93.1 | 99.2 | 98.7 | 68.6 | 89.9 | 95.4 |
1∶1∶5 | 207.18×109 | 30.60×106 | 92.9 | 99.2 | 98.7 | 68.4 | 89.8 | 95.2 |
1∶1∶6 | 208.39×109 | 30.12×106 | 93.0 | 99.2 | 98.7 | 68.3 | 90.3 | 95.3 |
1∶1∶7 | 207.95×109 | 30.91×106 | 92.8 | 99.2 | 98.5 | 66.9 | 90.2 | 95.4 |
1∶1∶8 | 208.01×109 | 29.96×106 | 92.2 | 99.2 | 98.3 | 64.8 | 89.6 | 94.8 |
Tab.11
Ablation experiments on the RSOD dataset"
模型 | 说明 | 浮点运算数 | 参数量 | 网络搜索时间/h | mAP/(%) | AP50/(%) | AP75/(%) | APs/(%) | APm/(%) | APl/(%) |
---|---|---|---|---|---|---|---|---|---|---|
Model-1 | Baseline | 203.83×109 | 28.86×106 | 7.5 | 88.9 | 99.0 | 97.9 | 57.3 | 86.0 | 92.0 |
Model-2 | Baseline+渐进式进化算法 | 199.84×109 | 26.85×106 | 6.2 | 89.0 | 98.9 | 97.9 | 56.8 | 86.2 | 92.3 |
Model-3 | Baseline+FSResBlock | 209.61×109 | 30.33×106 | 9.2 | 92.9 | 99.2 | 98.6 | 68.3 | 90.1 | 95.4 |
Model-4 | Baseline+渐进式进化算法+FSResBlock | 207.63×109 | 30.29×106 | 8.1 | 93.1 | 99.2 | 98.7 | 68.6 | 89.9 | 95.4 |
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