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机器视觉

目标检测指标AP和mAP.

一、准确率&回归率

  • 术语表：
- 预测正例 预测反例
真值正例 TP FN
真值反例 FP TN
  • 准确率Precision公式表示：

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+

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Precison=\frac{TP}{TP+FP}

Precison=TP+FPTP​

  • 回归率Recall公式表示：

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Recall=\frac {TP}{TP+FN}

Recall=TP+FNTP​

​ Precision表示预测为正例的结果中有多少是对的，比如100个样本，预测正例为50个，正确的样本有TP=25个，预测错误的有FP=25个。那么Precision就等于50%。但考虑极端情况下，只使用准确率是不合适的，比如100个样本，预测正例1个，恰好真值为正例也是TP=1个，FP=0个，那么Precision=100%，但是有99个都被预测成了反例。Recall指标就是为了权衡这个问题，进而更好衡量真值正例的预测情况，还需要计算预测正例在真值正例（=TP+FN）中的比例。只有两个指标都很高的时候，说明结果才是好的。

二、AP的概念

  • P-R图

    以Recall为横轴，Precision为纵轴，并将每次的值画出连线，就可以得到P-R图。随着Recall增加，Precision会逐渐降低，并在某个值附近上下波动。

  • AP(Average Precision)

    AP为平均精度，使用积分的方式来计算PR曲线与坐标轴围成的面积：

    ∫

    0

    1

    f

    (

    r

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    d

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    \int_{0}^{1}f(r)\,{\rm d}r

    ∫01​f(r)dr
    实际操作不计算积分，而是对其进行平滑操作来简化计算，对PR曲线上的每个点，Precision取该点右侧最大的值，如图所示：

在这里插入图片描述

​ 计算出AP后，对所有类别的AP加和求平均就得到整个数据集上的mAP。

三、目标检测中的mAP

​ 不同的数据集会给出不同的mAP计算方法。

​ 目标检测算法最终会得到score1和bbox，按照score降序排序，通过设定score阈值来分出正类例和反类例（带有对类别的预测），因此评价mAP可以是在多个阈值尺度下分开的正反例来计算的：

  1. 【训练时】使用区域选择算法得到候选区域
  2. 【训练时】对于候选区，计算每一个候选区和标定框（真值）之间的IoU
  3. 【训练时】设定一个IoU阈值，大于这个阈值的候选区标定为正例，小于的标定为反例。得到一组测试集，及训练好的权重模型。
  4. 【预测时】将上面得到的测试集，通过分类器权重模型，算出每一个图片是正样本的score。
  5. 【预测时】设定一个score阈值，大于等于这个值的方框类别视为预测正例，反之为预测反例。
  6. 【预测时】根据第五步的结果算出Precision和Recall。
  7. 调整score的阈值，得到某个类别对应Recall从0到1对应的Precision值，计算出所有类的AP后就可以计算mAP了。

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Score Threshold

TP/FP/FN/TN

Recall

Precision

Impact on

Impact on

Impact on

Indirectly Impact

Indirectly Impact

Score Threshold

TP/FP/FN/TN

Recall

Precision

  • Interplolated AP（略）

  • MS COCO mAP

     

    A

    P

    50

    \ AP_{50}

     AP50​：IoU阈值为0.5时的AP测量值

     

    A

    P

    75

    \ AP_{75}

     AP75​：IoU阈值为0.75时的AP测量值

     

    A

    P

    S

    \ AP_{S}

     APS​： 像素面积小于

     

    3

    2

    2

    \ 32^{2}

     322的目标框的AP测量值（小尺度）

     

    A

    P

    M

    \ AP_{M}

     APM​：像素面积在

     

    3

    2

    2

    −

    9

    6

    2

    \ 32^{2}-96^{2}

     322−962之间的目标框的AP测量值（中尺度）

     

    A

    P

    L

    \ AP_{L}

     APL​：像素面积大于

     

    9

    6

    2

    \ 96^{2}

     962的目标框的AP测量值（大尺度）

​

Reference

目标检测的指标AP与mAP - 知乎 (zhihu.com)

  1. 每一个预测框都对应着N+1个score，也就是对应着N个类别预测和一个background，选择score得分最高的那个作为这个框的类别。 ↩︎

版权声明：本文为CSDN博主「拔牙的萌萌鼠」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/Harry_Jack/article/details/121264284

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