# 3.8 Anchor Boxes

对象检测存在的一个问题是每个格子只能检测出一个对象，如果想让一个格子检测出多个对象，可以使用**anchor box**

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/49b7d68a17e89dd109f96efecc223f5a.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/49b7d68a17e89dd109f96efecc223f5a.png)

> 行人的中点和汽车的中点都落入到同一个格子中

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/e001f5f3d2afa76a1c3710bd60bcad00.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/e001f5f3d2afa76a1c3710bd60bcad00.png)

**anchor box**的思路是：预先定义两个不同形状的**anchor box**，把预测结果和这两个**anchor box**关联起来

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/2e357b5b92122660c550dcfb0901519c.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/2e357b5b92122660c550dcfb0901519c.png)

定义类别标签：

$$
y= \begin{bmatrix} p\_{c} & b\_{x} & b\_{y} \&b\_{h} & b\_{w} & c\_{1} & c\_{2} & c\_{3} & p\_{c} & b\_{x} & b\_{y} & b\_{h} & b\_{w} \&c\_{1} & c\_{2} & c\_{3} \end{bmatrix}^{T}
$$

前面的$$p\_{c},b\_{x},b\_{y},b\_{h},b\_{w},c\_{1},c\_{2},c\_{3}$$（绿色方框标记的参数）是和**anchor box 1**关联的8个参数，后面的8个参数（橙色方框标记的元素）是和**anchor box 2**相关联

行人：$$p\_{c}= 1,b\_{x},b\_{y},b\_{h},b\_{w},c\_{1} = 1,c\_{2} = 0,c\_{3} = 0$$

车子的边界框更像**anchor box 2**，($$p\_{c}= 1,b\_{x},b\_{y},b\_{h},b\_{w},c\_{1} = 0,c\_{2} = 1,c\_{3} = 0$$)

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/e94aa7ea75300ea4692682b179834bb4.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/e94aa7ea75300ea4692682b179834bb4.png)

现在每个对象都分配到对象中点所在的格子中，以及分配到和对象形状交并比最高的**anchor box**中。然后观察哪个**anchor box**和实际边界框（编号1，红色框）的交并比更高

编号1对应同时有车和行人，编号3对应只有车：

[![](https://github.com/fengdu78/deeplearning_ai_books/raw/master/images/322b15fe615c739ebd1d36b669748618.png)](https://github.com/fengdu78/deeplearning_ai_books/blob/master/images/322b15fe615c739ebd1d36b669748618.png):

**anchor box**是为了处理两个对象出现在同一个格子的情况，实践中这种情况很少发生，特别用的是19×19网格

怎么选择**anchor box：**

* 一般手工指定**anchor box**形状，可以选择5到10个**anchor box**形状，覆盖到想要检测的对象的各种形状
* 更高级的是使用**k-平均算法**，将两类对象形状聚类，选择最具有代表性的一组**anchor box**


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