To address the problem that it is difficult to balance the accuracy and speed of vehicle detection in current traffic monitoring scenarios, this paper proposes a lightweight vehicle detection algorithm based on YOLOv8. First, the detection speed and accuracy of the model are improved by replacing the Neck layer of the original model with slimneck; second, the SimAM attention mechanism is introduced to strengthen the key features of the vehicle and suppress the non-key features; and finally, the WIoU loss function is adopted to achieve lower regression error. The experimental results on the UA-DETRAC dataset show that, compared with the base model, the improved method in this paper improves 2.66% and 7.14% in the two indexes of mAP and FPS, which effectively improves the problem of lower vehicle detection accuracy in complex traffic scenarios and achieves faster detection speed.
Improved YOLOv8-based Vehicle Detection Method for Road Monitoring and Surveillance
2023-09-22
1724150 byte
Conference paper
Electronic Resource
English
Automatic driving road condition monitoring method based on improved YOLOv8
European Patent Office | 2024
|Ship Detection Based on Improved YOLOv8 Algorithm
IEEE | 2024
|