PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm
With the development of deep convolutional neural networks, the effect of pedestrian detection has been rapidly improved. However, there are still many problems in small target pedestrian detection, for example noise (such as light) interference, target occlusion, and low detection accuracy. In orde...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10044092/ |
| _version_ | 1797894959743893504 |
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| author | Kaihui Li Yuan Zhuang Jinling Lai Yunhui Zeng |
| author_facet | Kaihui Li Yuan Zhuang Jinling Lai Yunhui Zeng |
| author_sort | Kaihui Li |
| collection | DOAJ |
| description | With the development of deep convolutional neural networks, the effect of pedestrian detection has been rapidly improved. However, there are still many problems in small target pedestrian detection, for example noise (such as light) interference, target occlusion, and low detection accuracy. In order to solve the above problems, based on YOLOv4 algorithm, this paper proposes an improved small target pedestrian detection algorithm named PF_YOLOv4. The algorithm is improved in three aspects on the basis of the YOLOv4 algorithm: firstly, a soft thresholding module is added to the residual structure of the backbone network to perform noise reduction process on interference factors, such as light to enhance the robustness of the algorithm; secondly, the depthwise separable convolution replaces the traditional convolution in the YOLOv4 residual structure, to reduce the number of network model parameters; finally, the Convolutional Block Attention Module (CBAM) is added after the output feature map of the backbone network to enhance of the network feature expression. Experimental results show that the PF_YOLOv4 algorithm outperforms most of the state-of-the-art algorithms in detecting small target pedestrians. The mean Average Precision (mAP) of the PF_YOLOv4 algorithm is 2.35% higher than that of the YOLOv4 algorithm and 9.67% higher than that of the YOLOv3 algorithm, while the detection speed is slightly higher than that of YOLOv4 algorithm. |
| first_indexed | 2024-04-10T07:18:51Z |
| format | Article |
| id | doaj.art-ba8e86db42904786928c1c895865a446 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T07:18:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ba8e86db42904786928c1c895865a4462023-02-25T00:00:23ZengIEEEIEEE Access2169-35362023-01-0111171971720610.1109/ACCESS.2023.324498110044092PFYOLOv4: An Improved Small Object Pedestrian Detection AlgorithmKaihui Li0https://orcid.org/0000-0002-2249-3497Yuan Zhuang1Jinling Lai2Yunhui Zeng3https://orcid.org/0000-0003-3398-6884Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaFaculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaFaculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaFaculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaWith the development of deep convolutional neural networks, the effect of pedestrian detection has been rapidly improved. However, there are still many problems in small target pedestrian detection, for example noise (such as light) interference, target occlusion, and low detection accuracy. In order to solve the above problems, based on YOLOv4 algorithm, this paper proposes an improved small target pedestrian detection algorithm named PF_YOLOv4. The algorithm is improved in three aspects on the basis of the YOLOv4 algorithm: firstly, a soft thresholding module is added to the residual structure of the backbone network to perform noise reduction process on interference factors, such as light to enhance the robustness of the algorithm; secondly, the depthwise separable convolution replaces the traditional convolution in the YOLOv4 residual structure, to reduce the number of network model parameters; finally, the Convolutional Block Attention Module (CBAM) is added after the output feature map of the backbone network to enhance of the network feature expression. Experimental results show that the PF_YOLOv4 algorithm outperforms most of the state-of-the-art algorithms in detecting small target pedestrians. The mean Average Precision (mAP) of the PF_YOLOv4 algorithm is 2.35% higher than that of the YOLOv4 algorithm and 9.67% higher than that of the YOLOv3 algorithm, while the detection speed is slightly higher than that of YOLOv4 algorithm.https://ieeexplore.ieee.org/document/10044092/Small target pedestrian detectionsoft thresholdingdepthwise separable convolutionconvolutional block attention module |
| spellingShingle | Kaihui Li Yuan Zhuang Jinling Lai Yunhui Zeng PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm IEEE Access Small target pedestrian detection soft thresholding depthwise separable convolution convolutional block attention module |
| title | PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm |
| title_full | PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm |
| title_fullStr | PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm |
| title_full_unstemmed | PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm |
| title_short | PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm |
| title_sort | pfyolov4 an improved small object pedestrian detection algorithm |
| topic | Small target pedestrian detection soft thresholding depthwise separable convolution convolutional block attention module |
| url | https://ieeexplore.ieee.org/document/10044092/ |
| work_keys_str_mv | AT kaihuili pfyolov4animprovedsmallobjectpedestriandetectionalgorithm AT yuanzhuang pfyolov4animprovedsmallobjectpedestriandetectionalgorithm AT jinlinglai pfyolov4animprovedsmallobjectpedestriandetectionalgorithm AT yunhuizeng pfyolov4animprovedsmallobjectpedestriandetectionalgorithm |