摘要
在复杂天气条件下,由于大雾或者强光照射造成车牌成像的失真,使传统基于图像的车牌识别方法难以准确识别车牌信息。文中基于何恺明提出的Dark Channel Prior和直方图均衡方法能有效消除天气带来的干扰。并通过形态学方法对被噪声影响的图像进行复原、修正,最后通过基于BP神经网络对车牌信息进行识别。实验证明,文中方法对恶劣天气条件下的车牌信息还原清晰,有着较好的识别效果。
In complex weather conditions,the distortion of the license plate imaging caused by the fog or glare makes it difficult to accurately identify plate information by the traditional image-based license plate identification method.By the Dark Channel Prior proposed by He Kaiming and histogram equalization method,the interference caused by weather conditions can be effectively eliminated,followed by image restoration and correction by morphology method,and finally,the license plate information can be recognized accurately based on the BP neural network.Experimental results show that the restored license images are clear and the recognition effect is good under the bad weather conditions.
出处
《电子科技》
2012年第11期91-94,共4页
Electronic Science and Technology
关键词
图像增强
二值形态学
图像分割
BP神经网络
车牌识别
image enhancement
binary morphology
image segmentation
BP neural network
license plate recognition
