Road vehicle detection based on improved YOLOv3-SPP algorithm
Aiming at the problem of low detection accuracy or missing detection caused by dense vehicles and small scale of distant vehicles in the visual detection of urban road scenes, an improved YOLOv3-SPP algorithm was proposed to optimize the activation function and take DIOU-NMS Loss as the boundary fra...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024046/ |
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| _version_ | 1841540043774099456 |
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| author | Tao WANG Hao FENG Rongxin MI Lin LI Zhenxue HE Yiming FU Shu WU |
| author_facet | Tao WANG Hao FENG Rongxin MI Lin LI Zhenxue HE Yiming FU Shu WU |
| author_sort | Tao WANG |
| collection | DOAJ |
| description | Aiming at the problem of low detection accuracy or missing detection caused by dense vehicles and small scale of distant vehicles in the visual detection of urban road scenes, an improved YOLOv3-SPP algorithm was proposed to optimize the activation function and take DIOU-NMS Loss as the boundary frame loss function to enhance the expression ability of the network.In order to improve the feature extraction ability of the proposed algorithm for small targets and occluding targets, the void convolution module was introduced to increase the receptive field of the target.Based on the experimental results, the proposed algorithm improves the mAP by 1.79% when detecting vehicle targets, and also effectively reduce the missing phenomenon when detecting tight vehicle targets. |
| format | Article |
| id | doaj-art-6060cab7ae1449c18a87f6264aa8efe0 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-6060cab7ae1449c18a87f6264aa8efe02025-01-14T06:22:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-02-0145687859383199Road vehicle detection based on improved YOLOv3-SPP algorithmTao WANGHao FENGRongxin MILin LIZhenxue HEYiming FUShu WUAiming at the problem of low detection accuracy or missing detection caused by dense vehicles and small scale of distant vehicles in the visual detection of urban road scenes, an improved YOLOv3-SPP algorithm was proposed to optimize the activation function and take DIOU-NMS Loss as the boundary frame loss function to enhance the expression ability of the network.In order to improve the feature extraction ability of the proposed algorithm for small targets and occluding targets, the void convolution module was introduced to increase the receptive field of the target.Based on the experimental results, the proposed algorithm improves the mAP by 1.79% when detecting vehicle targets, and also effectively reduce the missing phenomenon when detecting tight vehicle targets.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024046/vehicle detectionYOLOv3-SPP algorithmactivation functionatrous convolutiondeep learning |
| spellingShingle | Tao WANG Hao FENG Rongxin MI Lin LI Zhenxue HE Yiming FU Shu WU Road vehicle detection based on improved YOLOv3-SPP algorithm Tongxin xuebao vehicle detection YOLOv3-SPP algorithm activation function atrous convolution deep learning |
| title | Road vehicle detection based on improved YOLOv3-SPP algorithm |
| title_full | Road vehicle detection based on improved YOLOv3-SPP algorithm |
| title_fullStr | Road vehicle detection based on improved YOLOv3-SPP algorithm |
| title_full_unstemmed | Road vehicle detection based on improved YOLOv3-SPP algorithm |
| title_short | Road vehicle detection based on improved YOLOv3-SPP algorithm |
| title_sort | road vehicle detection based on improved yolov3 spp algorithm |
| topic | vehicle detection YOLOv3-SPP algorithm activation function atrous convolution deep learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024046/ |
| work_keys_str_mv | AT taowang roadvehicledetectionbasedonimprovedyolov3sppalgorithm AT haofeng roadvehicledetectionbasedonimprovedyolov3sppalgorithm AT rongxinmi roadvehicledetectionbasedonimprovedyolov3sppalgorithm AT linli roadvehicledetectionbasedonimprovedyolov3sppalgorithm AT zhenxuehe roadvehicledetectionbasedonimprovedyolov3sppalgorithm AT yimingfu roadvehicledetectionbasedonimprovedyolov3sppalgorithm AT shuwu roadvehicledetectionbasedonimprovedyolov3sppalgorithm |