An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes
At present, the difficulty of pedestrian detection has been dramatically increased because of some problems, such as the dark or exposed illumination, bad weather, serious occlusion, large difference size of pedestrians and blurred images in complex visual scenes.Therefore, an improved YOLOv4 algori...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2021-08-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021198/ |
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| _version_ | 1841528982785228800 |
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| author | Shuai KANG Jianwu ZHANG Zunjie ZHU Guofeng TONG |
| author_facet | Shuai KANG Jianwu ZHANG Zunjie ZHU Guofeng TONG |
| author_sort | Shuai KANG |
| collection | DOAJ |
| description | At present, the difficulty of pedestrian detection has been dramatically increased because of some problems, such as the dark or exposed illumination, bad weather, serious occlusion, large difference size of pedestrians and blurred images in complex visual scenes.Therefore, an improved YOLOv4 algorithm was proposed, which improved the detection performance of pedestrian detection in complex visual scenes, aiming at the problems of low accuracy and highly missed detection rate.Firstly, the self-annotation data set pedetrian were constructed.Secondly, the hybrid dilated convolution (HDC) was added into the backbone network to improve the ability of pedestrian feature extraction.Finally, in order to obtain more detailed feature, the spatial jagged dilated convolution (SJDC) structure was proposed to replace the spatial pyramid pooling structure.The experimental results show that the average precision (AP) of the proposed algorithm can achieve 90.08%.The proposed algorithm can substantially improve AP by 7.2%, and the log-average miss rate (LAMR) reduce by 13.69% compared with the original YOLOv4 algorithm. |
| format | Article |
| id | doaj-art-ffa1b7ac39e9485eaaf5129fc35881a2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-ffa1b7ac39e9485eaaf5129fc35881a22025-01-15T03:32:31ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-08-0137465659814401An improved YOLOv4 algorithm for pedestrian detection in complex visual scenesShuai KANGJianwu ZHANGZunjie ZHUGuofeng TONGAt present, the difficulty of pedestrian detection has been dramatically increased because of some problems, such as the dark or exposed illumination, bad weather, serious occlusion, large difference size of pedestrians and blurred images in complex visual scenes.Therefore, an improved YOLOv4 algorithm was proposed, which improved the detection performance of pedestrian detection in complex visual scenes, aiming at the problems of low accuracy and highly missed detection rate.Firstly, the self-annotation data set pedetrian were constructed.Secondly, the hybrid dilated convolution (HDC) was added into the backbone network to improve the ability of pedestrian feature extraction.Finally, in order to obtain more detailed feature, the spatial jagged dilated convolution (SJDC) structure was proposed to replace the spatial pyramid pooling structure.The experimental results show that the average precision (AP) of the proposed algorithm can achieve 90.08%.The proposed algorithm can substantially improve AP by 7.2%, and the log-average miss rate (LAMR) reduce by 13.69% compared with the original YOLOv4 algorithm.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021198/complex visual scenesYOLOv4hybrid dilated convolutionspatial jagged dilated convolution |
| spellingShingle | Shuai KANG Jianwu ZHANG Zunjie ZHU Guofeng TONG An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes Dianxin kexue complex visual scenes YOLOv4 hybrid dilated convolution spatial jagged dilated convolution |
| title | An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes |
| title_full | An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes |
| title_fullStr | An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes |
| title_full_unstemmed | An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes |
| title_short | An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes |
| title_sort | improved yolov4 algorithm for pedestrian detection in complex visual scenes |
| topic | complex visual scenes YOLOv4 hybrid dilated convolution spatial jagged dilated convolution |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021198/ |
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