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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.