<p>Object detection is a regression of the bounding box (rectangle) around the object. In this way, the two ways you suggest are equivalent.</p>

<p>What I suggest you to look at is <a href="https://medium.com/towards-data-science/lane-detection-with-deep-learning-part-1-9e096f3320b7" rel="nofollow noreferrer">lane detection</a> for self-driving cars - a well studied problem. It seems that this is a very relevant task (if not the same), so its solution should work for you as well.</p>

CC BY-SA 3.0
2017-10-07T23:21:31.150Z
2017-10-07T23:21:31.150Z
Maxim
<p>I have some images of the empty parking as shown below. </p>

<p><a href="https://i.sstatic.net/sDQLM.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/sDQLM.png" alt="Empty parking lot"></a></p>

<p>I 'd like to use deep learning to extract the parking spots. But in the beginning,am confused whether there are several ways to do the following; </p>

<ol>
<li>Treat each parking spot as an object and use object detection to find
the parking space.</li>
<li>Treat the parking spot as a few key points, instead of finding the
spot, try to find the coordinates of key points on the parking line.</li>
</ol>

<p>Any ideas on how would I work on these two methods  </p>

<neural-networks><convolutional-neural-networks><image-recognition><object-recognition>
How to detect the empty parking spots?
user824624
# How to detect the empty parking spots?

(Asked by: username_0 on 2017-09-26)

I have some images of the empty parking as shown below.

[![Empty parking lot](https://i.sstatic.net/sDQLM.png)](https://i.sstatic.net/sDQLM.png)

I 'd like to use deep learning to extract the parking spots. But in the beginning,am confused whether there are several ways to do the following;

1. Treat each parking spot as an object and use object detection to find
   the parking space.
2. Treat the parking spot as a few key points, instead of finding the
   spot, try to find the coordinates of key points on the parking line.

Any ideas on how would I work on these two methods

**Tags:** neural-networks, convolutional-neural-networks, image-recognition, object-recognition


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## 1 answers

### Answer by username_1 on 2017-10-07. Score: 2

Object detection is a regression of the bounding box (rectangle) around the object. In this way, the two ways you suggest are equivalent.

What I suggest you to look at is [lane detection](https://medium.com/towards-data-science/lane-detection-with-deep-learning-part-1-9e096f3320b7) for self-driving cars - a well studied problem. It seems that this is a very relevant task (if not the same), so its solution should work for you as well.


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