Abstract
In order to detect the vehicle stopping behaviors more accurately in highway tunnel, a detection method based on the improved YOLOv3 is proposed. Firstly, a highway tunnel data set is produced and the YOLOv3 object detection model is deepened to improve the network structure and accurately detect vehicles in the tunnel. Then the Deep SORT algorithm is used to track the vehicle, and the vehicle speed is calculated. The speed threshold is used to determine whether a stopping behavior has occurred. The experimental results show that, compared with the original model, the optimized YOLOv3 model in this paper improves the mAP on VOC-vehicle data set and Tunnel-vehicle data set. Finally, a vehicle detection model with 98.19% mAP is obtained. In addition, based on the improved YOLOv3, the detection method of highway tunnel vehicle stopping is tested on the highway tunnel video and the results show that the detection of the vehicle stopping behaviors can be completed effectively.
| Translated title of the contribution | Detection Method of Highway Tunnel Vehicle Stopping Based on Improved YOLOv3 |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 234-239 |
| Number of pages | 6 |
| Journal | Computer Engineering and Applications |
| Volume | 57 |
| Issue number | 23 |
| DOIs | |
| Publication status | Published - 1 Dec 2024 |
| Externally published | Yes |