Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information
Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5490 |
| _version_ | 1797552660313800704 |
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| author | Yi Xiao Xinqing Wang Peng Zhang Fanjie Meng Faming Shao |
| author_facet | Yi Xiao Xinqing Wang Peng Zhang Fanjie Meng Faming Shao |
| author_sort | Yi Xiao |
| collection | DOAJ |
| description | Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case. |
| first_indexed | 2024-03-10T16:04:14Z |
| format | Article |
| id | doaj.art-f94a771df32c4edaa6f398006c28d752 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:04:14Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f94a771df32c4edaa6f398006c28d7522023-11-20T15:03:40ZengMDPI AGSensors1424-82202020-09-012019549010.3390/s20195490Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual InformationYi Xiao0Xinqing Wang1Peng Zhang2Fanjie Meng3Faming Shao4Department of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaDepartment of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaDepartment of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaDepartment of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaDepartment of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, ChinaDeep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case.https://www.mdpi.com/1424-8220/20/19/5490object detectionFaster R-CNNcontextskip poolingguided anchor RPN |
| spellingShingle | Yi Xiao Xinqing Wang Peng Zhang Fanjie Meng Faming Shao Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information Sensors object detection Faster R-CNN context skip pooling guided anchor RPN |
| title | Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information |
| title_full | Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information |
| title_fullStr | Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information |
| title_full_unstemmed | Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information |
| title_short | Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information |
| title_sort | object detection based on faster r cnn algorithm with skip pooling and fusion of contextual information |
| topic | object detection Faster R-CNN context skip pooling guided anchor RPN |
| url | https://www.mdpi.com/1424-8220/20/19/5490 |
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