Low Light Image Enhancement in License Plate Recognition using URetinex-Net and TRBA

The license plate recognition system currently in use is susceptible to interference from the external environment and performs poorly in low-light conditions. This paper presents a solution for license plate recognition under a low-light environment. We adopted URetinex-Net methods that unfold an optimization issue into a learnable network to decompose a low illumination image into reflectance and illumination layers. We also adopted TRBA, an end-to-end recognition method involving no character segmentation. The experimental results show that the accuracy of the night environment of the proposed method is 80.11% increased by 5.11% compared to without the low light image enhancement method.