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<h1>
[Convolutional Neural Networks] week3. Object detection
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<time datetime="2017-11-27T00:00:00+01:00"><i class="fa fa-calendar"></i> Mon, 27 Nov 2017</time>
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<span class="label label-default">Series</span>
Part 12 of «Andrew Ng Deep Learning MOOC»
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目录
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<div class="panel-boy">
<div id="toc"><ul><li><a class="toc-href" href="#object-localization" title="Object Localization">Object Localization</a></li><li><a class="toc-href" href="#landmark-detection" title="Landmark Detection">Landmark Detection</a></li><li><a class="toc-href" href="#object-detection" title="Object Detection">Object Detection</a></li><li><a class="toc-href" href="#convolutional-implementation-of-sliding-windows" title="Convolutional Implementation of Sliding Windows">Convolutional Implementation of Sliding Windows</a></li><li><a class="toc-href" href="#bounding-box-predictions" title="Bounding Box Predictions">Bounding Box Predictions</a></li><li><a class="toc-href" href="#intersection-over-union" title="Intersection Over Union">Intersection Over Union</a></li><li><a class="toc-href" href="#non-max-suppression" title="Non-max Suppression">Non-max Suppression</a></li><li><a class="toc-href" href="#anchor-boxes" title="Anchor Boxes">Anchor Boxes</a></li><li><a class="toc-href" href="#yolo-algorithm" title="YOLO Algorithm">YOLO Algorithm</a></li><li><a class="toc-href" href="#optional-region-proposals" title="(Optional) Region Proposals">(Optional) Region Proposals</a></li></ul></div>
</div>
</div>
<h3 id="object-localization">Object Localization</h3>
<p>Classification VS. Localization VS. Detection<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image.png"/> </p>
<p><strong>classification with localization</strong><br/>
Apart from softmax output (for classification), <em>add 4 more outputs of bounding box</em>: <code>b_x, b_y, b_h, b_w</code>.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image002.png"/> </p>
<p><strong>Defining target label y in localization</strong><br/>
label format:<br/>
<code>P_c</code> indicating if there's any object<br/>
bounding box: <code>b_x, b_y, b_h, b_w</code><br/>
class proba: <code>c_1, c_2, c_3</code> </p>
<p>Loss function: squared error<br/>
if y_1=P_c=1: loss = square error (y, y_hat)<br/>
if y_1=P_c=0: loss = (y_1 - y_1_hat)^2<br/>
can use different loss function for different components, but sq-loss works in practice. </p>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image003.png"/> </p>
<h3 id="landmark-detection">Landmark Detection</h3>
<p>"landmark": important points in image. → let NN output their coords. </p>
<p>e.g. recognize coord of eye's corner or points along the eye/nose/mouth<br/>
→ specify a number of landmarks<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image005.png"/> </p>
<h3 id="object-detection">Object Detection</h3>
<p><strong>sliding windows detection</strong><br/>
example: car detection.<br/>
training image: <em>closely-croped</em> image<br/>
in prediciton: use sliding window and pass to ConvNet; use window of different size.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image007.png"/><br/>
Sliding window is OK with pre-DL algos.<br/>
disadvantage: computation cost too high — each window's crop ran <em>independently</em> through ConvNet.<br/>
→ sliding window also can be implemented "convolutionally" — some computation can be cached. </p>
<h3 id="convolutional-implementation-of-sliding-windows">Convolutional Implementation of Sliding Windows</h3>
<p><strong>Turning FC layer into conv layers</strong><br/>
example: last conv/maxpool layer: size=5<em>5<br/>
→ </em>replace FC(output_dim=400) by 400 5<em>5 filters</em><br/>
→ replace next FC layer by 1<em>1 filters<br/>
→ replace softmax layer by 1</em>1 filters and activation.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image008.png"/> </p>
<p><strong>conv implementation of sliding window</strong><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image009.png"/><br/>
example: training image 14<em>14</em>3, testing image 16<em>16</em>3<br/>
instead of corping image to 14<em>14 and feed to ConvNet, </em>feed the larger picture directly to ConvNet*.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image010.png"/> </p>
<p>→ <em>output contains results of all patches</em>!<br/>
⇒ instead of computing each sliding window sequentially, can get all results with a single pass of the full image!!<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image012.png"/><br/>
problem: bounding box position is not accurate. </p>
<h3 id="bounding-box-predictions">Bounding Box Predictions</h3>
<p>To output more accurate bounding boxes: aspect-ration no longer 1:1.<br/>
<strong>YOLO algorithm</strong><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image013.png"/><br/>
"You Only Look Once"<br/>
For each grid cell: apply image classification with bouding boxes (described in 1st section, 8 outputs).<br/>
needs labelled data: assign each obj to the grid where its center is in.<br/>
<em>output volume: 3</em>3<em>8</em><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image014.png"/><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image015.png"/> </p>
<p>Also: a lot of computation shared, efficient ⇒ possible to do real-time. </p>
<p>note: bounding box annotation in YOLO <em>can be out of [0,1] range.</em><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image016.png"/> </p>
<h3 id="intersection-over-union">Intersection Over Union</h3>
<p><strong>Evaluating</strong> object localization:<br/>
→ intersection over union (IoU) function = size(intesection) / size(union) = <em>measure of overlap of two bounding boxes.</em><br/>
"correct" if IoU >= 0.5<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image017.png"/> </p>
<h3 id="non-max-suppression">Non-max Suppression</h3>
<p>Problem: algo might detect the same obj multiple times.<br/>
example:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image018.png"/><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image019.png"/><br/>
each bouding box has a confidence score — keep the max bouding box, suppress the overlapping ones.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image020.png"/> </p>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image021.png"/> </p>
<h3 id="anchor-boxes">Anchor Boxes</h3>
<p>Problem: each grid detects only one obj → can a grid <em>detect multiple obj</em> ? → use anchor boxes. </p>
<p>In data labeling: predefine 2 shapes (anchor boxes); use 8 sets of 8 outputs for each anchor box.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image022.png"/> </p>
<p>Compare with previous: </p>
<ul>
<li>previous: each obj assigned to the grid which contains its mid point </li>
<li>now each obj assigned to (cell, anchorbox): cell=the grid which contains its mid point; anchor_box=<em> the anchorbox that has highest IoU with the labelled bounding box</em>. </li>
</ul>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image023.png"/> </p>
<p>In practice: choose 5~10 anchor boxes by hand; or use Kmeans on object's shapes. </p>
<h3 id="yolo-algorithm">YOLO Algorithm</h3>
<p>Put all components together.<br/>
example: </p>
<ul>
<li>detecting pedestrian/car/motercycle. (4 classes) </li>
<li>grid: 3*3 </li>
<li>2 anchor boxes </li>
</ul>
<p>→ <strong>Preparing training set</strong><br/>
y shape = 3*3*2*8<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image024.png"/> </p>
<p>train a ConvNet on this with output_dim = 3<em>3</em>16<br/>
→ <strong>making predictions</strong><br/>
2*8 outputs for each of the 9 grids<br/>
→ <strong>nonmax supression for each class</strong><br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image025.png"/> </p>
<h3 id="optional-region-proposals">(Optional) Region Proposals</h3>
<p><img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image026.png"/><br/>
Region proposal algo (R-CNN): less often than YOLO.<br/>
Sliding window disadvantage: many regions are not interesting.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image027.png"/><br/>
⇒ select just a few windows<br/>
first run segmentation algo, then run CNN on bounding box of blobs.<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image028.png"/><br/>
→ still quite slow<br/>
faster variants:<br/>
<img alt="" class="img-responsive" src="../images/Ng_DLMooc_c4wk3/pasted_image029.png"/> </p>
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<div id="toc"><ul><li><a class="toc-href" href="#object-localization" title="Object Localization">Object Localization</a></li><li><a class="toc-href" href="#landmark-detection" title="Landmark Detection">Landmark Detection</a></li><li><a class="toc-href" href="#object-detection" title="Object Detection">Object Detection</a></li><li><a class="toc-href" href="#convolutional-implementation-of-sliding-windows" title="Convolutional Implementation of Sliding Windows">Convolutional Implementation of Sliding Windows</a></li><li><a class="toc-href" href="#bounding-box-predictions" title="Bounding Box Predictions">Bounding Box Predictions</a></li><li><a class="toc-href" href="#intersection-over-union" title="Intersection Over Union">Intersection Over Union</a></li><li><a class="toc-href" href="#non-max-suppression" title="Non-max Suppression">Non-max Suppression</a></li><li><a class="toc-href" href="#anchor-boxes" title="Anchor Boxes">Anchor Boxes</a></li><li><a class="toc-href" href="#yolo-algorithm" title="YOLO Algorithm">YOLO Algorithm</a></li><li><a class="toc-href" href="#optional-region-proposals" title="(Optional) Region Proposals">(Optional) Region Proposals</a></li></ul></div>
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