建议进行算法的选择 [英] Advice for algorithm choice

问题描述

我必须这样做，试图扫描车辆的外形并检测它，扫描将一个传感器被称为汽车扫描仪他们只是50光束灯，每束进行什么类型的车辆项目受体和发射，因为它是图中所示：

I have to do a project that tries to scan the shape of the vehicles and detect what type of vehicle it is , the scanning will performed with a sensors called "vehicle scanner" they are just 50 beams of lights, each beam with receptor and emission as it is shown in the picture:

我从传感器获得每个波束（块或疏通）的原始沙爹，并与连续扫描，我们可以创建车辆的可能非常低清晰度图片。

I get from the sensors the raw sate of each beam (block or unblock) and with that continuous scanning we can create a probably very low res image of the vehicle.

我的问题是，我可以用什么算法/技术检测和识别车辆的形状，我们要算的车轮，如果能，试图找出如果形状是汽车或卡车或皮卡等，至少我们要算的车轮。

My question is what algorithms/technique I can use to detect and identify the shape of the vehicle, we want to count the wheels, and if we can, try to identify if that shape is a car or a truck or pickup etc., at least we want to count the wheels.

我正在考虑训练神经网络，但也许可能是这种模式的检测，我可以用一个简单的方法，我不知道。 任何其他建议/意见将大大AP preciated

I am considering training a neural network, but perhaps could be a simpler approach for this kind of pattern detection that I can use and I am unaware of. Any other suggestion/advice will be much appreciated

推荐答案

一个标准的神经网络将是一个合理的选择，并会的工作，但一个卷积神经网络（CNN）很可能是最好的选择（看到这样一个快速的解释）。细胞神经网络是很好的图像识别，因为它们的稀疏连接允许空间局部相关这意味着它们推广到新的数据集更有效（即他们考虑到在靠近彼此的投入之间的关系）比标准的神经网络，并且也越来越快训练。

A standard neural network would be a reasonable choice and would work, however a convolutional neural network (CNN) would probably be the best choice (see this for a quick explanation). CNNs are great for image recognition since their sparse connectivity allows for spatially local correlation (i.e. they take into account the relationships between inputs within close proximity to one another) meaning that they generalise to new data-sets more effectively than standard neural nets, and are also faster to train.

为了检测轮的数量，人们可分割低分辨率输入到一个数重叠'车轮尺寸补丁，然后使用每个补丁作为输入到CNN的已受过训练来检测车轮。由于存在的CNN的返回true可绕同一车轮多个补丁的可能性，接近检查需要被执行，以使​​每个本地真的补丁导致总计数器的仅单个递增。这可以通过识别具有最高输出节点激活所述局部斑块来完成，并通过preventing该修补的圆周内的任何其它补丁从影响总计数器。

In order to detect the number of wheels, one could split the low res input into a number of overlapping 'wheel sized' patches, then use each patch as input to a CNN which has been trained to detect wheels. Since there is the possibility of the CNN returning true for multiple patches around the same wheel, a proximity checker would need to be implemented so that each of the local 'true' patches causes only a single incrementation of the total counter. This could be done by identifying the local patch with the highest output node activation, and by preventing any other patch within the circumference of this patch from affecting the total counter.

识别形状作为汽车或卡车将实际上是一个更简单的任务，因为在整个图像可被送入上训练的选择$ P $对 - 分类车辆图像的CNN的。这将有可能来解决的挤压/通过的充实的训练数据集随机的挤压/拉伸变形拉伸速度的影响。对于就如何设置参数，在CNN，看到<一href="http://stackoverflow.com/questions/24509921/how-do-you-decide-the-parameters-of-a-convolutional-neural-network-for-image-cla/25421946#25421946">how你决定卷积神经网络的图像分类的参数。

Identifying the shape as a car or truck would in fact be a simpler task as the entire image could be fed to a CNN trained on a selection of pre-classified vehicle images. It would be possible to work around the squashing/stretching effects of speed by augmenting the training datasets with random squashing/stretching deformations. For advise on how to setup the parameters in a CNN, see how do you decide the parameters of a convolutional neural network for image classification.

由于细胞神经网络如何有效证据，看看在的大型视觉识别挑战2012（LSVRC）。 LSVRC是一个图像分类的竞争，其中的竞争对手竞争，实现最低的分类错误上任意选择256×256的图像。获奖的网络，命名为监督，通过CNN模型实现了闭合的竞争对手几乎一半的错误。细胞神经网络还举办了许多文字识别任务精度最高的纪录，例如 MNIST数字识别任务在模型中获得了99.8％的精度 - 这死敌人类识别率的精度

As proof of how effective CNNs are, take a look at the results of the Large Scale Visual Recognition Challenge 2012 (LSVRC). LSVRC was an image classification competition where competitors competed to achieve the lowest classification error on an arbitrary selection of 256x256 images. The winning network, named Supervision, achieved almost half the error of its closes competitor by using the CNN model. CNNs also hold the record for the highest accuracy on many text recognition tasks, for example the MNIST digit recognition task in which the model scored an accuracy of 99.8% - an accuracy which rivals human recognition rates.

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