# 3.3 目标检测（Object detection）

通过卷积网络进行对象检测，采用的是基于滑动窗口的目标检测算法

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/2f4e567978bb62fcbec093887de37783.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/2f4e567978bb62fcbec093887de37783.png)

构建汽车检测算法步骤：

1. 首先创建一个标签训练集，$$x$$和$$y$$表示适当剪切的汽车图片样本，一开始可以使用适当剪切的图片，就是整张图片$$x$$几乎都被汽车占据，使汽车居于中间位置，并基本占据整张图片
2. 开始训练卷积网络，输入这些适当剪切过的图片（编号6），卷积网络输出$$y$$，0或1表示图片中有汽车或没有汽车

训练完这个卷积网络，用它来实现滑动窗口目标检测，具体步骤如下：

1.首先选定一个特定大小的窗口，将红色小方块输入卷积神经网络，卷积网络开始判断红色方框内有没有汽车

[\
![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/2ac2ab6dcdcc0fe26a9833ff9da49bd2.png)](https://legacy.gitbook.com/book/baozou/neural-networks-and-deep-learning/edit#)

2.滑动窗口目标检测算法继续处理第二个图像，红色方框稍向右滑动之后的区域，并输入给卷积网络，再次运行卷积网络，然后处理第三个图像，依次重复操作，直到这个窗口滑过图像的每一个角落

[\
![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/c55f22f302899d5f9d77bef958465660.png)](https://legacy.gitbook.com/book/baozou/neural-networks-and-deep-learning/edit#)

思路是以固定步幅移动窗口，遍历图像的每个区域，把这些剪切后的小图像输入卷积网络，对每个位置按0或1进行分类

3.重复上述操作，选择一个更大的窗口，截取更大的区域，并输入给卷积神经网络处理，输出0或1

[\
![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/34507c03fbda16049faeb3caf075fe50.png)](https://legacy.gitbook.com/book/baozou/neural-networks-and-deep-learning/edit#)

4.再以某个固定步幅滑动窗口，重复以上操作，遍历整个图像，输出结果

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/f2b6d5bfedc5298160bc2628544e315c.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/f2b6d5bfedc5298160bc2628544e315c.png)

5.第三次重复操作，选用更大的窗口

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/c14524aa0534ed78c433e1cd0a8dff50.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/c14524aa0534ed78c433e1cd0a8dff50.png)

这样不论汽车在图片的什么位置，总有一个窗口可以检测到

这种算法叫作滑动窗口目标检测：以某个步幅滑动这些方框窗口遍历整张图片，对这些方形区域进行分类，判断里面有没有汽车

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/ef8afff4e50fc1c50a46b8443f1d6976.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/ef8afff4e50fc1c50a46b8443f1d6976.png)

滑动窗口目标检测算法缺点：**计算成本**

* 如果选用的步幅很大，会减少输入卷积网络的窗口个数，粗糙间隔尺寸可能会影响性能
* 如果采用小粒度或小步幅，传递给卷积网络的小窗口会特别多，这意味着超高的计算成本


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