【新能源车牌识别】基于模板匹配算法实现新能源车牌识别附Matlab代码

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🔥 内容介绍

随着社会的不断发展和科技的进步，新能源汽车的数量逐渐增加，为了更好地管理和监控这些车辆，新能源车牌识别技术应运而生。新能源车牌识别是指利用计算机视觉和图像处理技术，对新能源车辆的车牌进行自动识别和识别。在这篇博客中，我们将重点介绍基于模板匹配算法实现新能源车牌识别的技术原理和应用。

模板匹配算法是一种常用的图像处理技术，它通过将待识别物体的模板与输入图像进行比对，找到最佳匹配的位置。在新能源车牌识别中，首先需要收集大量的新能源车牌样本，并将其转化为模板。然后，系统会将实时捕获的车辆图像与模板进行比对，找到最佳匹配的车牌位置，并进行识别。

模板匹配算法实现新能源车牌识别具有以下优点：

1. 算法简单易懂：模板匹配算法不需要复杂的数学模型和算法，易于理解和实现。

2. 识别速度快：由于算法简单，识别速度较快，适用于实时监控和管理。

3. 适用性广泛：模板匹配算法适用于各种不同类型的新能源车牌，具有较强的通用性。

除了以上优点外，模板匹配算法也存在一些不足之处，比如对光照、角度、遮挡等因素较为敏感，容易受到外界环境的影响。因此，在实际应用中，需要结合其他图像处理和机器学习技术，提高新能源车牌识别的准确性和鲁棒性。

新能源车牌识别技术在交通管理、停车场管理、智能安防等领域具有广泛的应用前景。通过实现对新能源车辆的自动识别和监控，可以提高管理效率，减少人力成本，为城市交通和环境保护做出积极贡献。

总的来说，基于模板匹配算法实现新能源车牌识别是一种简单、快速且有效的技朋方案，具有较高的实用性和推广价值。随着技术的不断进步和应用场景的不断拓展，相信新能源车牌识别技术将会在未来发挥越来越重要的作用。

📣 部分代码

function [rectx,recty,area,perimeter] = minboundrect(x,y,metric)% minboundrect: Compute the minimal bounding rectangle of points in the plane% usage: [rectx,recty,area,perimeter] = minboundrect(x,y,metric)%% arguments: (input)%  x,y - vectors of points, describing points in the plane as%        (x,y) pairs. x and y must be the same lengths.%%  metric - (OPTIONAL) - single letter character flag which%        denotes the use of minimal area or perimeter as the%        metric to be minimized. metric may be either 'a' or 'p',%        capitalization is ignored. Any other contraction of 'area'%        or 'perimeter' is also accepted.%%        DEFAULT: 'a'    ('area')%% arguments: (output)%  rectx,recty - 5x1 vectors of points that define the minimal%        bounding rectangle.%%  area - (scalar) area of the minimal rect itself.%%  perimeter - (scalar) perimeter of the minimal rect as found%%% Note: For those individuals who would prefer the rect with minimum% perimeter or area, careful testing convinces me that the minimum area% rect was generally also the minimum perimeter rect on most problems% (with one class of exceptions). This same testing appeared to verify my% assumption that the minimum area rect must always contain at least% one edge of the convex hull. The exception I refer to above is for% problems when the convex hull is composed of only a few points,% most likely exactly 3. Here one may see differences between the% two metrics. My thanks to Roger Stafford for pointing out this% class of counter-examples.%% Thanks are also due to Roger for pointing out a proof that the% bounding rect must always contain an edge of the convex hull, in% both the minimal perimeter and area cases.%%% Example usage:%  x = rand(50000,1);%  y = rand(50000,1);%  tic,[rx,ry,area] = minboundrect(x,y);toc%%  Elapsed time is 0.105754 seconds.%%  [rx,ry]%  ans =%      0.99994  -4.2515e-06%      0.99998      0.99999%   2.6441e-05            1%  -5.1673e-06   2.7356e-05%      0.99994  -4.2515e-06%%  area%  area =%      0.99994%%% See also: minboundcircle, minboundtri, minboundsphere%%% Author: John D'Errico% E-mail: woodchips@rochester.rr.com% Release: 3.0% Release date: 3/7/07% default for metricif (nargin<3) || isempty(metric)  metric = 'a';elseif ~ischar(metric)  error 'metric must be a character flag if it is supplied.'else  % check for 'a' or 'p'  metric = lower(metric(:)');  ind = strmatch(metric,{'area','perimeter'});  if isempty(ind)    error 'metric does not match either ''area'' or ''perimeter'''  end    % just keep the first letter.  metric = metric(1);end% preprocess datax=x(:);y=y(:);% not many error checks to worry aboutn = length(x);if n~=length(y)  error 'x and y must be the same sizes'end% start out with the convex hull of the points to% reduce the problem dramatically. Note that any% points in the interior of the convex hull are% never needed, so we drop them.if n>3   edges = convhull(x,y);  %edges = convhull(x,y,{'Qt'});  % 'Pp' will silence the warnings  % exclude those points inside the hull as not relevant  % also sorts the points into their convex hull as a  % closed polygon    x = x(edges);  y = y(edges);    % probably fewer points now, unless the points are fully convex  nedges = length(x) - 1;elseif n>1  % n must be 2 or 3  nedges = n;  x(end+1) = x(1);  y(end+1) = y(1);else  % n must be 0 or 1  nedges = n;end% now we must find the bounding rectangle of those% that remain.% special case small numbers of points. If we trip any% of these cases, then we are done, so return.switch nedges  case 0    % empty begets empty    rectx = [];    recty = [];    area = [];    perimeter = [];    return  case 1    % with one point, the rect is simple.    rectx = repmat(x,1,5);    recty = repmat(y,1,5);    area = 0;    perimeter = 0;    return  case 2    % only two points. also simple.    rectx = x([1 2 2 1 1]);    recty = y([1 2 2 1 1]);    area = 0;    perimeter = 2*sqrt(diff(x).^2 + diff(y).^2);    returnend% 3 or more points.% will need a 2x2 rotation matrix through an angle thetaRmat = @(theta) [cos(theta) sin(theta);-sin(theta) cos(theta)];% get the angle of each edge of the hull polygon.ind = 1:(length(x)-1);edgeangles = atan2(y(ind+1) - y(ind),x(ind+1) - x(ind));% move the angle into the first quadrant.edgeangles = unique(mod(edgeangles,pi/2));% now just check each edge of the hullnang = length(edgeangles);area = inf;perimeter = inf;met = inf;xy = [x,y];for i = 1:nang  % rotate the data through -theta   rot = Rmat(-edgeangles(i));  xyr = xy*rot;  xymin = min(xyr,[],1);  xymax = max(xyr,[],1);    % The area is simple, as is the perimeter  A_i = prod(xymax - xymin);  P_i = 2*sum(xymax-xymin);    if metric=='a'    M_i = A_i;  else    M_i = P_i;  end    % new metric value for the current interval. Is it better?  if M_i<met    % keep this one    met = M_i;    area = A_i;    perimeter = P_i;        rect = [xymin;[xymax(1),xymin(2)];xymax;[xymin(1),xymax(2)];xymin];    rect = rect*rot';    rectx = rect(:,1);    recty = rect(:,2);  endend% get the final rect% all done​end % mainline end

⛳️ 运行结果

🔗 参考文献

[1] 赵瑞.运动模糊车牌识别系统的设计与实现[D].青岛科技大学,2015.DOI:10.7666/d.Y2774125.

[2] 赵瑞.运动模糊车牌识别系统的设计与实现[D].青岛科技大学,2016.

[3] 刘静,周静华,苏俊连,等.基于模板匹配的车牌字符识别算法实现[J].科技信息:科学·教研, 2007(24):334-335.DOI:10.3969/j.issn.1001-9960.2007.24.022.

[4] 邹明明,卢迪.基于改进模板匹配的车牌字符识别算法实现[J].国外电子测量技术, 2010(1):4.DOI:10.3969/j.issn.1002-8978.2010.01.017.

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