Sliding window protocol

Deep learning

Operating system

Pattern recognition (psychology)

Artificial intelligence

Object detection

Window (computing)

License

Computer vision

Computer science

Image and Vision Computing

Automatic License Plate Recognition (ALPR) is an important research topic in the intelligent transportation system and image recognition fields. In this work, we address the problem of car license plate detection using a You Only Look Once (YOLO)-darknet deep learning framework. In this paper, we use YOLO's 7 convolutional layers to detect a single class. The detection method is a sliding-window process. The object is to recognize Taiwan's car license plates. We use an AOLP dataset which contained 6 digit car license plates. The sliding window detects each digit of the license plate, and each window is then detected by a single YOLO framework. The system achieves approximately 98.22% accuracy on license plate detection and 78% accuracy on license plate recognition. The system executes a single detection recognition phase, which needs around 800 ms to 1 s for each input image. The system is also tested with different condition complexities, such as rainy background, darkness and dimness, and different hues and saturation of images.

Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning