Vehicle Detection and Tracking Based on Improved YOLOv8
With the rapid increase in transportation pressure, the demand for efficient traffic recognition and tracking systems is growing. Traditional methods have certain limitations when dealing with complex situations in traffic scenes, such as large weights and insufficient detection accuracies, et al. T...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10870217/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823857145767002112 |
|---|---|
| author | Yunxiang Liu Shujun Shen |
| author_facet | Yunxiang Liu Shujun Shen |
| author_sort | Yunxiang Liu |
| collection | DOAJ |
| description | With the rapid increase in transportation pressure, the demand for efficient traffic recognition and tracking systems is growing. Traditional methods have certain limitations when dealing with complex situations in traffic scenes, such as large weights and insufficient detection accuracies, et al. Therefore, we propose a novel method based on YOLOv8n. Firstly we introduced SCC_Detect based on SCConv on the detection head to reduce the computation of redundant features. Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. The BotSORT tracker is embedded in the period of inference, which achieves more accurate and stable recognition and tracking results in the traffic scene. The experimental results show that the proposed model reduces parameters and weight by approximately 36.5% and 25% respectively at the expense of only 0.2% [email protected] compared with YOLOv8n on the UA-DETRAC dataset. In terms of tracking, MOTA, IDF1 and MOTP of the BoTSORT algorithm on the test video were superior to those of DeepSORT and ByteTrack. The accuracy was improved, and the number of lost tracks was reduced. It has a high practical value and application prospects in traffic detection and deployment. |
| format | Article |
| id | doaj-art-9183036b7fc04590b13d35f6a5cf1bd5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9183036b7fc04590b13d35f6a5cf1bd52025-02-12T00:01:35ZengIEEEIEEE Access2169-35362025-01-0113247932480310.1109/ACCESS.2025.353855610870217Vehicle Detection and Tracking Based on Improved YOLOv8Yunxiang Liu0https://orcid.org/0009-0001-2736-9524Shujun Shen1https://orcid.org/0009-0005-0926-4429Department of Computer Information Science and Engineering, Shanghai Institute of Technology, Shanghai, ChinaDepartment of Computer Information Science and Engineering, Shanghai Institute of Technology, Shanghai, ChinaWith the rapid increase in transportation pressure, the demand for efficient traffic recognition and tracking systems is growing. Traditional methods have certain limitations when dealing with complex situations in traffic scenes, such as large weights and insufficient detection accuracies, et al. Therefore, we propose a novel method based on YOLOv8n. Firstly we introduced SCC_Detect based on SCConv on the detection head to reduce the computation of redundant features. Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. The BotSORT tracker is embedded in the period of inference, which achieves more accurate and stable recognition and tracking results in the traffic scene. The experimental results show that the proposed model reduces parameters and weight by approximately 36.5% and 25% respectively at the expense of only 0.2% [email protected] compared with YOLOv8n on the UA-DETRAC dataset. In terms of tracking, MOTA, IDF1 and MOTP of the BoTSORT algorithm on the test video were superior to those of DeepSORT and ByteTrack. The accuracy was improved, and the number of lost tracks was reduced. It has a high practical value and application prospects in traffic detection and deployment.https://ieeexplore.ieee.org/document/10870217/Vehicle trackingmulti-object tracklightweight modelYOLOv8vehicle detectionBotSORT |
| spellingShingle | Yunxiang Liu Shujun Shen Vehicle Detection and Tracking Based on Improved YOLOv8 IEEE Access Vehicle tracking multi-object track lightweight model YOLOv8 vehicle detection BotSORT |
| title | Vehicle Detection and Tracking Based on Improved YOLOv8 |
| title_full | Vehicle Detection and Tracking Based on Improved YOLOv8 |
| title_fullStr | Vehicle Detection and Tracking Based on Improved YOLOv8 |
| title_full_unstemmed | Vehicle Detection and Tracking Based on Improved YOLOv8 |
| title_short | Vehicle Detection and Tracking Based on Improved YOLOv8 |
| title_sort | vehicle detection and tracking based on improved yolov8 |
| topic | Vehicle tracking multi-object track lightweight model YOLOv8 vehicle detection BotSORT |
| url | https://ieeexplore.ieee.org/document/10870217/ |
| work_keys_str_mv | AT yunxiangliu vehicledetectionandtrackingbasedonimprovedyolov8 AT shujunshen vehicledetectionandtrackingbasedonimprovedyolov8 |