如何对滑动窗口对象检测中的True Negatives进行分类？ [英] How to categorize True Negatives in sliding window object detection?

问题描述

我收集了我的图像检测算法的结果。所以基本上我做的是，从一组图像（大小为320×480），我会运行一个滑动窗口的64×128通过它，并在一定数量的预定义比例。

我明白：

• True Positiveives =当我检测到的窗口重叠（在定义的交叉点大小/


• 假阳性=当算法给出正窗口，这是在真实的外面。


• False Negatives =当它没有给出正窗口，而地面实况注释声明有一个对象。

True Negatives ？这些真正的负面的所有窗口，我的分类器给我否定的结果？这听起来很奇怪，因为我一次滑动一个小窗口（64x128）4个像素，我有大约8个不同的尺度用于检测。

或者，我准备了一组纯负图片（没有对象/人类所有），其中我只是滑过，如果在这些图像中有一个或多个阳性检测，我会把它计为False Negative，反之亦然？

解决方案

我一直看到以下四个词： p>

• 假阴性;结果应为正数，但为负数。


• 假阳性;结果应为负值，但为正值。


• 正确;结果应为正数且为正数。


• 真阴性;结果应为负值，为负值。

在您的情况下，如果我理解正确，您正在尝试检测是否有对象在您的图像。因此，假阴性意味着有一个对象（结果应该是正的），但算法没有检测到它（因此返回否定）。一个真正的负面是简单的算法正确地说明它检查的区域不持有一个对象。

你可以选择忽略负值，但是这些可以用于进一步训练你的算法（例如，使用一种寻找两者的算法，而不是设置不被识别为假的一切）。

I'm gathering results from my image detector algorithm. So basically what I do is that, from a set of images (with the size of 320 x 480), I would run a sliding window of 64x128 thru it, and also under a number of predefined scales.

I understand that:

• True Positives = when my detected window overlaps (within defined intersection size / centroid) with the ground-truth (annotated bounding boxes)
• False Positives = when the algorithm gives me positive windows, which are outside of the grond truth.
• False Negatives = when it failed me to give positive window, while the ground truth annotation states that there's an object.

But what about True Negatives ? Are these true negatives all the windows that my classifier gives me negative results ? That sounds weird, since I'm sliding a small window (64x128) by 4 pixels at a time, and I've around 8 different scales used in detection. If I were to do that, then I'd have lots of true negatives per image.

Or do I prepare a set of pure negative images (no objects / human at all), where I just slide thru, and if there's one or more positive detections in each of these images, I'd count it as False Negative, and vice versa ?

Here's an example image (with green rects as the ground truth)

解决方案

I've always seen the four terms as the following:

• False negative; Result should have been positive, but is negative.
• False positive; Result should have been negative, but is positive.
• True positive; Result should have been positive and is positive.
• True negative; Result should have been negative and is negative.

In your case, if I understand correctly, you are trying to detect if there are objects in your image. False negative would therefore mean that there was a object (result should be positive) but the algorithm did not detect it (and therefore returned negative). A true negative is simply the algorithm correctly stating that the area it checked does not hold an object.

You can choose to ignore negative values, but these could be used to further train your algorithm (Eg; using an algorithm that looks for both, instead of setting everything that is not recognised to false).

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