Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images
In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model,...
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/8/2618 |
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doaj-6163308d286246bc9c83d2ebe3e619102021-04-08T23:04:01ZengMDPI AGSensors1424-82202021-04-01212618261810.3390/s21082618Improved Mask R-CNN for Aircraft Detection in Remote Sensing ImagesQifan Wu0Daqiang Feng1Changqing Cao2Xiaodong Zeng3Zhejun Feng4Jin Wu5Ziqiang Huang6School of Physics and Optoelectronic Engineering, Xidian University, 2 South Taibai Road, Xi’an 710071, ChinaShandong Institute of Space Electronic Technology, Yantai 264670, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, 2 South Taibai Road, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, 2 South Taibai Road, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, 2 South Taibai Road, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, 2 South Taibai Road, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, 2 South Taibai Road, Xi’an 710071, ChinaIn recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP<sub>50</sub> and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.https://www.mdpi.com/1424-8220/21/8/2618Mask R-CNNself-calibrationDOTA datasetaircraftremote sensing image |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Qifan Wu Daqiang Feng Changqing Cao Xiaodong Zeng Zhejun Feng Jin Wu Ziqiang Huang |
| spellingShingle |
Qifan Wu Daqiang Feng Changqing Cao Xiaodong Zeng Zhejun Feng Jin Wu Ziqiang Huang Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images Sensors Mask R-CNN self-calibration DOTA dataset aircraft remote sensing image |
| author_facet |
Qifan Wu Daqiang Feng Changqing Cao Xiaodong Zeng Zhejun Feng Jin Wu Ziqiang Huang |
| author_sort |
Qifan Wu |
| title |
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images |
| title_short |
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images |
| title_full |
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images |
| title_fullStr |
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images |
| title_full_unstemmed |
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images |
| title_sort |
improved mask r-cnn for aircraft detection in remote sensing images |
| publisher |
MDPI AG |
| series |
Sensors |
| issn |
1424-8220 |
| publishDate |
2021-04-01 |
| description |
In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP<sub>50</sub> and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model. |
| topic |
Mask R-CNN self-calibration DOTA dataset aircraft remote sensing image |
| url |
https://www.mdpi.com/1424-8220/21/8/2618 |
| work_keys_str_mv |
AT qifanwu improvedmaskrcnnforaircraftdetectioninremotesensingimages AT daqiangfeng improvedmaskrcnnforaircraftdetectioninremotesensingimages AT changqingcao improvedmaskrcnnforaircraftdetectioninremotesensingimages AT xiaodongzeng improvedmaskrcnnforaircraftdetectioninremotesensingimages AT zhejunfeng improvedmaskrcnnforaircraftdetectioninremotesensingimages AT jinwu improvedmaskrcnnforaircraftdetectioninremotesensingimages AT ziqianghuang improvedmaskrcnnforaircraftdetectioninremotesensingimages |
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