License plate recognition methodology in complex scenarios based on CSCM-YOLOv8 and CSM-LPRNet.

ABSTRACT: License plate recognition technology is widely applied traffic management, parking monitoring, and electronic toll collection, among other fields. However, in complex scenarios, such as bright light, fog, rain, snow, and nighttime, there is an urgent need to improve the accuracy of license plate recognition and system robustness. To cope with the difficult problem of license plate recognition in complex scenarios, this study proposes a license plate recognition method based on CSCM-YOLOv8 and CSM-LPRNet. The CPA-Enhancer preprocessing module is used to optimize the input feature representation, and the upsampling quality is improved by the perceptual feature reorganization capability of the CARAFE upsampling module. The SEAM is embedded for adaptive weight allocation, thus enhancing the ca

SUBMITTER: Xiong W 

PROVIDER: S-EPMC12758793 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature