Research on Pedestrian Detection Based on the Multi-Scale and Feature-Enhancement Model

Abstract

Pedestrian detection represents one of the critical tasks of computer vision; however, detecting pedestrians can be compromised by problems such as the various scale of pedestrian features and cluttered background, which can easily cause a loss of accuracy. Therefore, we propose a pedestrian detection method based on the FCOS network. Firstly, we designed a feature enhancement module to ensure that effective high-level semantics are obtained while preserving the detailed features of pedestrians. Secondly, we defined a key-center region judgment to reduce the interference of background information on pedestrian feature extraction. By testing on the Caltech pedestrian dataset, the AP value is improved from 87.36% to 94.16%. The results of the comparison experiment illustrate that the model proposed in this paper can significantly increase the accuracy.