Robust license plate detection using image saliency

Inspired from the observation that license plates are very salient to human visual perception, we propose a novel license plate detection algorithm based on image saliency in this paper. The proposed algorithm consists of two parts. The first part segments out the characters on a license plate using an intensity saliency map with a high recall rate. The second part applies a sliding window on these characters to compute some saliency-related features to detect license plates. We test the robustness of our algorithm by applying it on a mixed data set with high diversity collected from four databases. The mixed data set has 1024 images composed of license plates of all states of the U.S. We achieve a detection rate of 90% with False Positive Per Image (FPPI) = 12%. The detection box given by our algorithm has high precision, which will be very helpful for many applications such as license plate recognition.