System Implementation of Multiple License Plate Detection And Correction On Wide-Angle Images Using an Instance Segmentation Network Model

The shift from traditional image processing to advanced deep learning has greatly improved license plate recognition, increasing its maturity and stability. Despite these advancements, challenges persist, including susceptibility to environmental factors like insufficient lighting, complex backgrounds, and low-resolution images. Detection of multiple license plates involves processing various targets characterized by distinct sizes, lighting conditions, occlusion, positions, and angles. Each license plate may be located at different positions and orientations within the image, significantly increasing the complexity of the detection process. The study, conducted in an indoor parking lot with a wide-angle camera, addresses perspective-warping uncertainties. Utilizing the Segmenting Objects by Locations version 2 (SOLOv2) SOLOv2 model enhances multiple license plate detection and segmentation, with the resulting mask aiding corner location and correction, leading to a 12% recognition rate increase. Notably, the study employs an instance segmentation network, enabling simultaneous recognition of multiple vehicle license plates, offering a significant advancement in overcoming complexities associated with diverse environmental conditions and camera angles.