AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN
Currently, the rapid development of the aviation industry has made the safety of the airport becomes more and more important. The most important part of this is the capability of discriminate the different type of objects correctly. However, the existing detection models have the problems of degrada...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10877834/ |
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| author | Zhige He Yuanqing He |
| author_facet | Zhige He Yuanqing He |
| author_sort | Zhige He |
| collection | DOAJ |
| description | Currently, the rapid development of the aviation industry has made the safety of the airport becomes more and more important. The most important part of this is the capability of discriminate the different type of objects correctly. However, the existing detection models have the problems of degradation, lacking of detection capability for deformed and small objects and single feature extraction, causing low detection accuracy. To overcome these problems, we design an object detection method for airport scene named AS-Faster-RCNN. Firstly the ResNet-101 substitute for VGG-16 as the backbone network to improve the ability of detecting small objects, prevent the degradation and enhance the ability of detecting the small objects. Secondly, The DCN (Deformable Convolution Network) is employed in the backbone to strengthen the ability of extracting features for deformed objects. Finally, the CBAM (Convolutional Block Attention Module) is added to the backbone to extract multidimensional features to enhance performance of the model. We design some experiemnts to prove the feasibility of the method and the results demonstrate the mAP(mean Average Precision) has increased by 5.3% comapred to the basline model, and compared with other object detection models, its mAP also increased to a certain extent. |
| format | Article |
| id | doaj-art-da5408f7276843dfb287e7f03b5fcd1f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-da5408f7276843dfb287e7f03b5fcd1f2025-08-20T03:15:48ZengIEEEIEEE Access2169-35362025-01-0113360503606410.1109/ACCESS.2025.353993010877834AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNNZhige He0https://orcid.org/0009-0001-4446-1816Yuanqing He1School of Computer Science, Civil Aviation Flight University of China, Guanghan, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan, ChinaCurrently, the rapid development of the aviation industry has made the safety of the airport becomes more and more important. The most important part of this is the capability of discriminate the different type of objects correctly. However, the existing detection models have the problems of degradation, lacking of detection capability for deformed and small objects and single feature extraction, causing low detection accuracy. To overcome these problems, we design an object detection method for airport scene named AS-Faster-RCNN. Firstly the ResNet-101 substitute for VGG-16 as the backbone network to improve the ability of detecting small objects, prevent the degradation and enhance the ability of detecting the small objects. Secondly, The DCN (Deformable Convolution Network) is employed in the backbone to strengthen the ability of extracting features for deformed objects. Finally, the CBAM (Convolutional Block Attention Module) is added to the backbone to extract multidimensional features to enhance performance of the model. We design some experiemnts to prove the feasibility of the method and the results demonstrate the mAP(mean Average Precision) has increased by 5.3% comapred to the basline model, and compared with other object detection models, its mAP also increased to a certain extent.https://ieeexplore.ieee.org/document/10877834/Airport sceneobjection detectionfaster-RCNNCBAMResNetDCN |
| spellingShingle | Zhige He Yuanqing He AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN IEEE Access Airport scene objection detection faster-RCNN CBAM ResNet DCN |
| title | AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN |
| title_full | AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN |
| title_fullStr | AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN |
| title_full_unstemmed | AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN |
| title_short | AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN |
| title_sort | as faster rcnn an improved object detection algorithm for airport scene based on faster r cnn |
| topic | Airport scene objection detection faster-RCNN CBAM ResNet DCN |
| url | https://ieeexplore.ieee.org/document/10877834/ |
| work_keys_str_mv | AT zhigehe asfasterrcnnanimprovedobjectdetectionalgorithmforairportscenebasedonfasterrcnn AT yuanqinghe asfasterrcnnanimprovedobjectdetectionalgorithmforairportscenebasedonfasterrcnn |