Vehicle target detection method based on improved YOLO V3 network model
For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1673.pdf |
| _version_ | 1797626143907512320 |
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| author | Qirong Zhang Zhong Han Yu Zhang |
| author_facet | Qirong Zhang Zhong Han Yu Zhang |
| author_sort | Qirong Zhang |
| collection | DOAJ |
| description | For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles. |
| first_indexed | 2024-03-11T10:06:18Z |
| format | Article |
| id | doaj.art-d688faf80f2f4bf5807a4c14cb2dcf7d |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-11T10:06:18Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-d688faf80f2f4bf5807a4c14cb2dcf7d2023-11-16T15:05:15ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e167310.7717/peerj-cs.1673Vehicle target detection method based on improved YOLO V3 network modelQirong Zhang0Zhong Han1Yu Zhang2School of Information Science and Technology, Qiongtai Normal University, Haikou, Hainan, ChinaSchool of Information Science and Technology, Qiongtai Normal University, Haikou, Hainan, ChinaCollege of Computer and Information Science, Southwest University, Chongqing, Chongqing, ChinaFor the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles.https://peerj.com/articles/cs-1673.pdfYOLO V3Vehicle detectionModel optimizationAerial positioning |
| spellingShingle | Qirong Zhang Zhong Han Yu Zhang Vehicle target detection method based on improved YOLO V3 network model PeerJ Computer Science YOLO V3 Vehicle detection Model optimization Aerial positioning |
| title | Vehicle target detection method based on improved YOLO V3 network model |
| title_full | Vehicle target detection method based on improved YOLO V3 network model |
| title_fullStr | Vehicle target detection method based on improved YOLO V3 network model |
| title_full_unstemmed | Vehicle target detection method based on improved YOLO V3 network model |
| title_short | Vehicle target detection method based on improved YOLO V3 network model |
| title_sort | vehicle target detection method based on improved yolo v3 network model |
| topic | YOLO V3 Vehicle detection Model optimization Aerial positioning |
| url | https://peerj.com/articles/cs-1673.pdf |
| work_keys_str_mv | AT qirongzhang vehicletargetdetectionmethodbasedonimprovedyolov3networkmodel AT zhonghan vehicletargetdetectionmethodbasedonimprovedyolov3networkmodel AT yuzhang vehicletargetdetectionmethodbasedonimprovedyolov3networkmodel |