摘要
针对车牌定位中存在的由于光线不足、环境恶劣、车牌背景不清晰、车牌磨损、图像倾斜等干扰因素导致的定位不准、车牌模糊、反应时间长等问题,文章通过对比目前的各种不同车牌定位技术,提出了改进激励函数和改善梯度估计精度的BP神经网络算法。重点说明了改进的具体过程和车牌定位的原理、方法和步骤,运用Hough算法对车牌倾斜度进行校正,借助常规的灰度图像感知方法,通过车牌定位的具体试验证明了该方法有较强的适应能力和学习速度快、车牌定位准确率高、鲁棒性好等优点,从而说明了该方法的可行性。
Aiming at a series of the problems in the License Plate Location, by comparing with tile existing License Plate Location techniques, this paper puts forward a BP Neural Network algorithm. The algorithm improves the sigmoid Exciting Function and Gradient Estimation Accuracy. Meanwhile, some factors, containing the lack of light, bad environment, unclear plate background, worn plate, image slope and other disturbing factors, lead to the inaccuracy of locating, faint plate and longer reaction time. The paper mainly describes the improvement procedure and the method theory for locating the plate, and revises the vehicle slope by means of the Hough algorithm. Then, the practical experiment and general gray image sensing method, prove that this method has some advantages, such as stronger adaptability, higher learning speed, higher accuracy, better robustness and so on. Simultaneously these results prove the feasibility of this method.
出处
《计算机仿真》
CSCD
2007年第2期240-243,共4页
Computer Simulation
关键词
车牌定位
反向传播神经网络
改进
激励函数
License plate location
Back - propagation neural network
Improvement
Exciting function
