Vehicle Violation Detection Based on Improved YOLOv8 in Highway Service Areas
In highway service areas,complex environments such as lighting and weather changes can cause a sharp decline in vehicle detection accuracy.In addition,factors such as the inclination angle of the camera and the height of installation can increase false-negative and false-positive rates.To this end,a vehicle violation detection algorithm based on the improved YOLOv8 is proposed for highway service areas.First,the feature pyramid pooling layer of the YOLOv8 network,a Dilated Space Pyramid Pooling(DSPP)module,and a DSPP based on branch Attention(DSPPA)module are constructed to reduce the loss of semantic information in the backbone.The Branch Attention(BA)mechanism in DSPPA assigns different weights to the branches with varying degrees of contribution,making the model focus more on features that are suitable for the target size.Second,a parking space allocation strategy based on global matching is designed to effectively reduce the false-negative and false-positive rates of illegal parking detection in situations involving tilted views and overlapping vehicles.The experimental results show that the improved algorithm reduces the false-negative rate of parking violation detection from 15％to 8％and the false-positive rate from 7.5％to 6.1％,demonstrating considerable performance improvement in vehicle violation detection.
万方数据
维普
