目标检测和计算机视觉中的mAP指标 [英] mAP metric in object detection and computer vision

问题描述

在计算机视觉和目标检测中，常见的评估方法是mAP. 它是什么以及如何计算?

In computer vision and object detection, the common evaluation method is mAP. What is it and how is it calculated?

推荐答案

引用来自上述 Zisserman论文-4.2结果评估(第11页):

首先，将重叠标准"定义为大于0.5的交叉重叠. (例如，如果预测的盒子相对于地面真盒子满足此标准，则将其视为检测对象).然后使用这种贪婪"方法在GT盒和预测盒之间进行匹配:

First an "overlap criterion" is defined as an intersection-over-union greater than 0.5. (e.g. if a predicted box satisfies this criterion with respect to a ground-truth box, it is considered a detection). Then a matching is made between the GT boxes and the predicted boxes using this "greedy" approach:

将方法输出的检测分配给地面真实对象 满足重叠条件的顺序按(降序)排列 置信度输出.图像中同一物体的多次检测 被认为是错误的检测，例如对单个物体进行5次检测 算作1次正确检测和4次错误检测

Detections output by a method were assigned to ground truth objects satisfying the overlap criterion in order ranked by the (decreasing) confidence output. Multiple detections of the same object in an image were considered false detections e.g. 5 detections of a single object counted as 1 correct detection and 4 false detections

因此，每个预测的框都是True或False. 每个地面真相框都是正正"的. 没有真否定词.

Hence each predicted box is either True-Positive or False-Positive. Each ground-truth box is True-Positive. There are no True-Negatives.

然后，平均精度是通过对精度调用曲线上的精度值进行平均来计算的，其中调用的范围为[0，0.1，...，1](例如11个精度值的平均值).更精确地说，我们考虑略微校正的PR曲线，其中对于每个曲线点(p，r)，如果存在不同的曲线点(p'，r')，使得p'> p和r'> = r ，我们用这些点的最大p'替换p.

Then the average precision is computed by averaging the precision values on the precision-recall curve where the recall is in the range [0, 0.1, ..., 1] (e.g. average of 11 precision values). To be more precise, we consider a slightly corrected PR curve, where for each curve point (p, r), if there is a different curve point (p', r') such that p' > p and r' >= r, we replace p with maximum p' of those points.

对于我来说，仍然不清楚的是对那些从未从未检测到的GT盒(即使置信度为0)进行了处理.这意味着存在某些召回值，精确度-召回曲线将永远无法达到，这使得上述平均精确度计算不确定.

What is still unclear to me is what is done with those GT boxes that are never detected (even if the confidence is 0). This means that there are certain recall values that the precision-recall curve will never reach, and this makes the average precision computation above undefined.

简短的回答:在无法召回的区域中，精度降至0.

Short answer: in the region where the recall is unreachable, the precision drops to 0.

解释此问题的一种方法是假设，当置信度的阈值接近0时，无限数量的预测的边界框会在整个图像上点亮.然后精度立即变为0(因为只有有限数量的GT盒)，并且召回率在此平坦曲线上不断增长，直到达到100％.

One way to explain this is to assume that when the threshold for the confidence approaches 0, an infinite number of predicted bounding boxes light up all over the image. The precision then immediately goes to 0 (since there is only a finite number of GT boxes) and the recall keeps growing on this flat curve until we reach 100%.

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