Displacement Feature Mapping for Vehicle License Plate Recognition Influenced by Haze Weather

License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally towards the minimum edges from the maximum region. Thus, the training input and detected edges are used to identify the displacement between observed image frames to extract and differentiate the license plate region from the other vehicle regions. The proposed method maps the similarity feature between the detected and identified vehicle regions. This aids in leveraging the plate recognition precision with a high F1 score. Thus, this technique achieves a 10.27% improvement in identification precision, a 10.57% increase in F1 score, and a 9.73% reduction in false positive rate compared to baseline methods under maximum displacement conditions caused by haze. The technique attains an identification precision of 95.68%, an F1 score of 94.68%, and a false positive rate of 4.32%, indicating robust performance under haze-affected settings.