A semantic feature enhancement network(SFENet)for target detection is presented to address the issue that high-frequency features,including picture edge details,are not effectively extracted in the target detection algorithm,resulting in poor detection results.To capture more information about the image edge details and further strengthen the network's understanding of the global semantic features,this paper uses octave convolution to construct a high-frequency semantic feature enhancement module.By adjusting the high and low-frequency components in the convolutional features,this module improves the image's high-frequency feature extraction and effectively increases the target detection accuracy.The experimental results show that the detection results of SFENet on the Pascal VOC07+12 dataset and the self-built conical bucket dataset improve the mean accuracy(mAP)by 0.34%and 0.37%,respectively,compared with YOLOv4.This network can be adapted to the fields of autonomous driving and machine vision and effectively applied to the environment perception task of the Formula Student Autonomous China(FSAC).
万方数据
维普
