<p>SegNet uses the 13 convolutional layers from VGG. (2+2+3+3+3)</p>

<p>Check <a href="http://ethereon.github.io/netscope/#/preset/vgg-16" rel="nofollow noreferrer">this visualization</a> and <a href="https://arxiv.org/abs/1409.1556" rel="nofollow noreferrer">the paper</a> for more information.</p>

<p>From the paper:</p>

<blockquote>
  <p>It is easy to see that a stack of two 3×3 conv. layers (without spatial pooling in between) has an effective receptive field of 5×5 such layers have a 7 × 7 effective receptive field. So what have we gained by using, for instance, a stack of three 3×3 conv. layers instead of a single 7×7 layer? First, we incorporate three non-linear rectification layers instead of a single one, which makes the decision function more discriminative. Second, we decrease the number of parameters: assuming that both the input and the output of a three-layer 3 × 3 convolution stack has C channels, the stack is parametrised by <a href="https://i.stack.imgur.com/1ikBj.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/1ikBj.gif" alt="enter image description here"></a> weights; at the same time, a single 7 × 7 conv. layer would require <a href="https://i.stack.imgur.com/5TImF.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/5TImF.gif" alt="enter image description here"></a> parameters, i.e. 81% more. This can be seen as imposing a regularisation on the 7 × 7 conv. filters, forcing them to have a decomposition through the 3 × 3 filters (with non-linearity injected in between).</p>
</blockquote>
Regarding two convolutional layers placed following each other in SegNet architecture
<p>In <a href="https://arxiv.org/pdf/1511.00561.pdf" rel="nofollow noreferrer">SegNet</a>, the architecture proposed by authors is shown as follows. <a href="https://i.stack.imgur.com/DhAjw.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/DhAjw.jpg" alt="enter image description here"></a>.</p>

<p>What confuses me is that there are two convolutional layers following with each other in each building block, just as what shown in the figure as 1 and 2. What are the major motivations to place convolution layers this way instead of aggregating them into a single convolutional layer?</p>