43 R CNNs, SSDs, and YOLO
Summary
TLDRThis video explores the evolution of object detection in computer vision, from traditional methods like HOG and SVM to deep learning approaches. It covers the breakthrough of R-CNNs, their evolution into Fast R-CNNs and Faster R-CNNs, and introduces Single Shot Detectors (SSDs) and the revolutionary YOLO algorithm. The video explains how these advanced detectors work, their speed and accuracy, and touches on metrics like Intersection over Union (IoU) and Mean Average Precision (mAP). It concludes with a look at the latest advancements in YOLO, setting the stage for implementing these detectors using Python and OpenCV.
Takeaways
- 🔎 **Object Detection Overview**: Object detection is a crucial part of computer vision that combines object classification and localization to identify and locate objects within images.
- 🐾 **Historical Context**: Early object detection methods like Haar cascade classifiers and Histogram of Gradients (HOG) with SVMs have been largely replaced by deep learning approaches due to their inefficiency with multiple object classes.
- 🚀 **Deep Learning Breakthrough**: In 2014, the introduction of Region-based Convolutional Neural Networks (R-CNNs) marked a significant advancement in object detection, achieving high performance in the PASCAL VOC challenge.
- 📈 **Selective Search Algorithm**: R-CNNs use selective search to propose regions of interest within an image, which are then classified by a CNN and refined using a linear regression for bounding boxes.
- 📏 **Intersection over Union (IoU)**: IoU is a metric used to evaluate the accuracy of object detection by measuring the overlap between the predicted bounding box and the ground truth.
- 🏎️ **Fast R-CNN**: An evolution of R-CNN, Fast R-CNN improved speed by reducing the need for multiple model training and execution, using region of interest (ROI) pooling.
- 🚀 **Faster R-CNN**: Building on Fast R-CNN, Faster R-CNN further increased speed by eliminating the need for selective search, thus speeding up the region proposal stage.
- 🏁 **Single Shot Detectors (SSDs)**: SSDs introduced multiscale features and default boxes to significantly increase detection speed, achieving near real-time performance with minimal accuracy loss.
- 🌐 **YOLO (You Only Look Once)**: YOLO revolutionized object detection by using a single neural network applied to the full image, allowing for fast and efficient detection by predicting bounding boxes and class probabilities directly.
- 🏆 **YOLOv3**: The latest version of YOLO, YOLOv3, includes improvements like multi-scale training and batch normalization, enhancing its ability to detect objects of various sizes and resolutions.
Q & A
What is object detection in the context of computer vision?
-Object detection is a crucial aspect of computer vision that combines object classification and localization. It not only identifies the presence of an object within an image but also determines the region or bounding box of the object, allowing for multi-class detection unlike earlier single-class detectors.
How do traditional non-learning methods for object detection compare to deep learning methods?
-Traditional methods such as Haar cascade classifiers and Histogram of Gradients (HOG) with Linear SVMs are considered outdated compared to deep learning methods. Deep learning object detectors significantly outperform these traditional methods, offering better accuracy and applicability in a wider range of scenarios.
What was the breakthrough in deep learning object detection in 2014?
-In 2014, the introduction of Regions with CNNs (R-CNNs) marked a significant breakthrough in deep learning object detection. R-CNNs achieved remarkably high performance in the PASCAL VOC challenge, a benchmark in computer vision for object detection.
How does the Selective Search algorithm contribute to object detection?
-The Selective Search algorithm contributes to object detection by segmenting the image into different regions based on similarities in color or texture. It proposes regions of interest that are then passed through a CNN for classification, thus streamlining the process of generating bounding box proposals.
What is Intersection over Union (IoU) and why is it important in object detection?
-Intersection over Union (IoU) is a metric used to measure the accuracy of an object detector by calculating the overlap between the predicted bounding box and the ground truth box. An IoU score above 0.5 is generally considered acceptable, indicating a good match between the predicted and actual object location.
How does the Mean Average Precision (mAP) metric help in evaluating object detectors?
-Mean Average Precision (mAP) is a metric used to evaluate the performance of object detectors, especially when dealing with multiple detections for the same object. It provides a measure of accuracy by considering both the precision and recall of the detector across various classes of objects.
What improvements did Fast R-CNN bring over the original R-CNN?
-Fast R-CNN improved upon the original R-CNN by reducing the computational overhead. It eliminated the need for separate models for feature extraction, classification, and bounding box regression by running the CNN only once over the entire image and then applying Region of Interest (ROI) pooling.
How do SSDs achieve near real-time performance in object detection?
-SSDs (Single Shot MultiBox Detectors) achieve near real-time performance by using multiscale features and default boxes, which allow for efficient processing without the need for region proposal networks. They also reduce the resolution of images fed into the classifier, which contributes to their speed.
What is the core concept behind YOLO (You Only Look Once) object detection?
-The core concept behind YOLO is to use a single neural network that processes the full image at once, allowing it to reason globally about the image content. It divides the image into a grid and predicts bounding boxes and class probabilities for each grid cell, which simplifies the detection process and enables fast performance.
How does YOLO's architecture differ from other object detection methods?
-YOLO's architecture differs by using a single, unified model that combines region proposal, object recognition, and classification into one full convolutional neural network. This design allows for fast and efficient object detection without the need for multiple stages or models, which is a departure from methods like R-CNNs and SSDs.
Outlines
🔎 Introduction to Object Detection
This paragraph introduces the concept of object detection as the 'holy grail' of computer vision, emphasizing its importance in identifying and localizing multiple classes of objects within images. It contrasts single-class object detection with the more complex multi-class detection, highlighting the inefficiency of traditional methods like sliding windows. The paragraph also briefly mentions the advent of deep learning object detectors like R-CNNs, SSDs, and YOLO, which have revolutionized the field by offering efficient and accurate detection across various object classes.
📊 Understanding Object Detection Metrics
The second paragraph delves into the metrics used to evaluate object detection models, focusing on the Intersection over Union (IoU) metric. It explains how IoU measures the overlap between the predicted bounding box and the ground truth, with an IoU of over 0.5 being considered acceptable. The discussion includes the challenges of handling multiple bounding boxes for the same object and introduces the Mean Average Precision (mAP) metric as a way to assess model performance in such scenarios. The paragraph also touches on the evolution of deep learning object detectors, starting from R-CNNs to Fast R-CNNs and Faster R-CNNs, each improving upon the previous in terms of speed and efficiency.
🚀 Speeding Up Object Detection with SSDs
Paragraph three discusses the Single Shot Detectors (SSDs) and their significant improvement in speed over the R-CNN family, achieving near real-time performance. It explains how SSDs use multiscale features and default boxes, along with reduced image resolution for classification, to enhance speed without a substantial loss in accuracy. The paragraph also mentions the trade-off between increasing the number of default boxes for better accuracy and the consequent decrease in speed. The discussion concludes with an overview of SSD's structure, including the feature map extractor and the convolutional filter used for object detection.
🏆 The Rise of YOLO: Real-Time Object Detection
The final paragraph introduces the You Only Look Once (YOLO) object detection framework, which uses a single neural network applied to the full image, allowing for global reasoning and real-time detection. It outlines the evolution of YOLO from its initial version to YOLOv3, highlighting improvements such as batch normalization, higher resolution training, and multi-scale training to enhance the detection of smaller objects. The paragraph also provides a brief overview of YOLO's architecture, consisting of 24 convolutional layers followed by fully connected layers, and encourages viewers to read the original paper for a deeper understanding.
Mindmap
Keywords
💡Object Detection
💡RCNNs (Regions with CNNs)
💡Selective Search
💡CNNs (Convolutional Neural Networks)
💡SVM (Support Vector Machine)
💡Bounding Box
💡Intersection over Union (IoU)
💡Faster R-CNN
💡SSD (Single Shot Detector)
💡YOLO (You Only Look Once)
Highlights
Introduction to deep learning object detectors like R-CNNs, SSDs, and YOLO.
Object detection is the 'holy grail' of computer vision, enabling multi-class detection.
Object detection combines classification and localization.
Traditional non-learning methods like HOG with SVM are outdated compared to deep learning detectors.
Deep learning object detectors are more efficient than sliding window approaches.
R-CNNs were a breakthrough in 2014, achieving high performance in the PASCAL VOC challenge.
Selective Search algorithm is used to generate region proposals for R-CNNs.
SVM is used to classify features extracted by CNNs in R-CNNs.
Linear regression is applied to refine the bounding boxes in R-CNNs.
Intersection over Union (IoU) is a key metric for evaluating object detection.
Mean Average Precision (mAP) is used to handle multiple detections of the same object.
Fast R-CNN improved the speed of R-CNNs by reducing the need for separate models.
Faster R-CNN further increased speed by eliminating the selective search bottleneck.
SSDs use multiscale features and default boxes for faster detection.
YOLO uses a single neural network applied to the full image for real-time object detection.
YOLO divides the image into an SxS grid and predicts bounding boxes and probabilities for each region.
YOLOv2 introduced batch normalization and was fine-tuned for higher resolutions.
YOLOv3 introduced multi-scale training for better detection of smaller objects.
SSDs and YOLO are currently among the top-performing object detectors.
Upcoming video will cover implementing object detection with SSDs and YOLO in Python using OpenCV.
Transcripts
hi and welcome to video where we take a
look at some deep learning
object detectors such as rcnns ssds
and yolo so let's start what exactly is
object detection well it is actually the
holy grail of computer vision
it allows us to do things like this and
you would have seen this before in our
face object detectors and pedestrian and
vehicle object detectors
in earlier videos however those were
limited to just
one class and if you were to add
multiple classes you would have to pile
on multiple object detections and that
would slow things down significantly
so we're taking a look at now some deep
learning some very advanced
object detectors that make things like
this possible this is pretty cool isn't
it
car dog horse person in the back over
here so what exactly is this now object
detection as i said in earlier chapters
is a mix of
object classification and localization
classification means determining what
the object is and localization means
identifying the region or the bounding
box of the object and it's used in face
detection quite often which we've seen
in previous chapters
now the difference with object detection
and classification
is that object detection doesn't just
tell you when an image contains a cat or
not but it actually tells you where the
cat order objects or
animals could be in the image so that's
pretty cool
now non-learning methods of object
detection included
what we discussed earlier which was
horror cascade classifiers and another
one we didn't discuss in earlier
chapters was histogram of gradients hog
with linear svms that support vector
machines it's a machine learning
classifier
and the reason we didn't discuss these
is because they are pretty much
outdated now because deep learning
object detectors
are significantly better and it can be
used in a wide variety of applications
that make it very powerful but the
reason why these aren't useful although
they are
technically very useful when you're
detecting just one object that's why
they're still very useful for face
detection
however when you try to apply this for
multiple objects
detectors it actually breaks down it
doesn't work too well and it can often
be quite slow and that's because it
involves
an approach called sliding windows where
you have to slide this window at various
scales across the image
and that's why it is not a very
efficient method
so in 2014 deep learning object
detectors had
a huge breakthrough with something
called regions with cnns or rcnns for
short
and this achieved remarkably high
performance in the pascal
voc challenge that is object detection
data set that has been used to compete
and score in computer vision journals
and researchers
to assess how good their object
detection methods are so basically
imagenet
variation of it and in 2014 as i said
our cnn's achieved remarkable success
now let's take a look at how our cnns
work so rcnn's attempted to solve the
exhaustive search problem
previously performed by sliding windows
by proposing
bounding boxes and passing these
extracted boxes to an image classifier
so now how do we get these bounding box
proposals and that's where we use an
algorithm called selective sig
so imagine we have an input image here
and we just extract
interesting regions here we send these
regions now to a cnn
to classify and then we get different
classifications of what it thinks it is
so now let's take a look at the
selective search algorithm
this attempts to segment the image into
different groups by combining
similar areas such as colors or textures
this could be
interpreted as blobs or contours and
proposes which of these regions
it is interesting using some of the
metrics within the classified algorithm
and it proposes these boxes here coming
out of this
segmentation type task and basically
feeds these
boxes here to the cnn to classify
one selective search found these
interesting boxes it takes these boxes
and passes it through the cnn as i said
before
one was trained on imagenet dataset so
with the imagenet dataset so it's
quite well tuned and well exposed to a
lot of data
and then what we do we don't actually
use the cnn directly for classification
all that we can
we then use the svm i said it's a
support vector machine classifier to
classify the cnn extracted features we
just compute the cnn features and feed
it into another classifier
and svm type classifier so after the
region proposal has been classified
we then use a simple linear regression
to generate a tighter bounding box
but what exactly makes a good box how do
we know
this box puzzle over this guy here is a
good box
now before we move on to other types of
deep learning object detectors
we're going to discuss some metrics and
how you assess object detectors
now this brings us to something called
intersection over union
or the iou metric so iou is defined as
the size of the union
over the size of the prediction box so
typically an iou over
0.5 is considered acceptable now let's
take a look at this metric in a little
bit more detail
imagine this green box over this car
here is our true
human labeled box to identify the car
remember
what we want in localized boxes we don't
want a box that predicts
half of the car and then it says at the
back wheel or we don't want a box that
covers
the car yes but covers a lot of extra
area around the car we want a box to be
nice and tight
exactly over the object in question that
we detected
so let's imagine this red box here is
the box
our object detector has proposed now it
looks fairly good it covers roughly
about 80 percent of the image
that's pretty good now let's take a look
at the one on the right
here this box which is actually covers a
lot more of the car covers almost 95
percent or more of the car
pretty much just missing out this little
little back piece here covers even more
so technically shouldn't this be
classified as a better box than this
well that's not exactly what we want
there is it
we don't want our box to be all over
here so that's how the iou metric
is developed it's the size of the union
over the size of the prediction box
so the union basically is this zone here
what is this zone here this zone is we
compute the area of this zone here
and the size of the prediction box is
put over here so
what we do okay this is the size
objection box is always going to be
bigger
so what this tells us is that we want
this prediction box
to cover as much of the green as
possible
without with being as small on the green
on the out as small as possible
over the green essentially hope you
understand that maybe i'm saying it
wrong
but essentially we just want this red
hair to cover
the green as much as possible without
being too
much over it or too much inside of it so
that's how we get this metric here
so anything over 0.5 is considered
fairly acceptable in my opinion
a lot of there are a lot more stricter
guidelines when it depends on type of
classifier you're building i mean
detective
but essentially 0.5 is a reasonable
score as you can see in this
box over here let's take a look at where
the iou would be
so the size of the union here looks
let's say estimated at 50 percent
maybe even a bit yeah 50 and then
besides the prediction box is definitely
maybe another 50 percent here
so you're going to end up with five
basically it's a fifty percent over a
hundred percent which is double the size
of this
so you're going to end up with something
like 0.5 whereas in this one
this one is basically you can say this
is 80 percent here
and this area that is on the outside is
probably like
0.6 of the ratio of this thing here so
you can understand how you get a much
higher iou
with this metric here now there's
another problem
when we have object vectors often times
we will have
multiple boxes being proposed over the
same object
and the object detector may not even
know it's doing it more than once
because remember it doesn't actually
know what's in the image it is
predicting interesting boxes in the
image
that it may or may not be correct it's
very hard to determine
for algorithm to determine is it two
cars packed side by side
is it one car so what we have to do is
if we have our ground shoot labels which
is the green
box here when we're testing our object
detectors you may often get
multiple windows proposed like this so
there's another metric called mean
average position or map for short
that we can use to get when we have
multiple boxes over the same thing
we can actually over the same true label
here ground truth
we can actually use this metric now to
determine the map score
and that determines the form of accuracy
of
our object detector so this one is
actually a bit confusing you can read
some pretty long blogs explaining this
metric that will help you understand
however for now just remember it's a
metric used to determine the performance
of
object detectors so now now that you
understand the metrics we can go back on
to the evolution of deep learning object
detectors
and let's take a look at something
called fast rcnn which is the evolution
of
our cnns by the same researchers and
what they did
they basically made some speed
improvements over the original rcnn
the original rcnn was quite slow because
it required three models to
train separately and use them in
conjunction when you're doing the
classification type work
execution in the end and this required
feature extraction
model an svm to predict a class and then
a linear regression to tighten the
bounding boxes in the end so you can see
in real time this would be quite slow so
fast rcnn solved this problem by
removing the overlap generated
now how what they did is that they ran
the scene and across the image just once
using a technique called
region of interest or roi pool so
essentially the problem the faster our
xenon solved was instead of running the
cnn
to extract features on all different
regions identified what they did sorry
they actually ran the cnn just once over
the entire image and then used those
features going forward into the second
and third
layers of models in the network so even
still that wasn't fast enough
and the researchers developed an even
faster rcnn and named it appropriately
faster or cnn however note there is no
fastest rcnn
just yet so what the researchers of the
microsoft research team did
to speed up our cnns or fast rcnns to
give it the faster flavor
was that they eliminated the bottleneck
that was involved when using selective
siege
so this sped up regent proposal
significantly as well
which now brings us to something called
single shot detectors or
ssds so we've just discussed and linked
the rcnn family and we've seen how
successful they can be and how they
evolved
however typically they still run at
roughly 7 frames per second
even on some fairly powerful hardware
however
ssds and even yellow are significantly
faster and you can actually see some of
the stats right here this is the fbs
as well you can see yellow and ssd is
topping the fps at 45 and 46 and you can
see the different scores
and in resolutions as well okay so how
did ssds improve speed so significantly
well
ssds use multiscale features and they
use something called default boxes
instead of looking at interesting
regions
and furthermore they actually dropped
resolution of the images that were fed
into the classifier itself
so this allowed ssds to achieve near
real-time speed performance
with almost no drop in accuracy and
sometimes they actually saw improved
accuracy when with well-trained ssds
so this is a general ssd structure it's
composed of two main parts
there's the feature map extractor and
vdd-16 was used
in the published paper but however
resonant and dead snap may provide
better results now
and then they used a convolutional
filter for the object detection part of
it
as well here so you can read the paper
in this link here if you want to get an
in-depth explanation of it but just to
summarize ssds are definitely faster
than faster rcnns however they are less
accurate in detecting
smaller objects now accuracy increases
if we increase the number of default
boxes
that allows us to get a more finer grid
on the image
however that actually slows down the
speed as well
and what it does with multi-scale
feature maps it allows it to improve the
optic detection
at various scales that's why we actually
can increase the accuracy
if we increase the density of the grid
so now
let's move on to yolo so the idea behind
yellow instead of using
a lot of different models and networks
and different
sections to do different things in the
object detector
yolo uses a single neural network that's
applied to the full image and this
allows yellow to reason about globally
across the image
when generating its predictions now it
is the direct development and evolution
of something called multibox
it basically takes multibox which was
used for region proposal
and turns it into object recognition and
then adds
a softmax layer in parallel with the box
regressor that combines everything into
a box classifier as well so you have the
entire object detector
built into it here so how it works it
divides the images into regions and
predicts boundary boxes and
probabilities for each region
so euler then uses a full convolutional
neural network allowing for inputs of
various sizes and i must say yolo is one
of the most impressive object detectors
that have
ever been built and so nice to use to
train
to evolve into different different
situations so now let's take a further
look at it and by the way if you want to
read and learn more about yolo you can
click this link here or go to the site
here to read the actual paper it's quite
good and actually
surprisingly entertaining as well so how
exactly does yolo work
well the image is firstly divided into
an s by s
grid and if the center of an object
falls into this grid cell
that cell is responsible for detecting
that object so let's say
this dog here falls into the center of
the cell here this cell is responsible
for
saying that this is a dog technically
however let's move on to the other steps
and you get a bigger picture of how it
works
so each grid then predicts a number of
bounding boxes and confidence scores for
those boxes and our confidence here is
defined
as the probability of an object
multiplied by the thresholded iu score
and iu scores of less than 0.5 are
typically just given a confidence
level of zero then by multiplying the
conditional
class probability and individual box
confidence predictions we get something
called the class specific confidence
score for each box
so you can see the steps here we have
all these bonding boxes here
you can see it proposes now a box region
around this dog here
and this is all based on the class
probability map and then once it gets
the final boxes here that it wants to
classify you can actually see the final
detections here
and the yellow is quite good and
effective in practice and quite
fast you can take a look at your
architecture here as well
or if you want as i said i encourage you
to read this paper here
now the architecture here has 24
convolutional
layers and followed by two fully
connected layers and
alternating one by one confidential
layers to reduce the feature spaces from
proceeding layers
now this may not make much sense to you
but you can read the paper to actually
understand the reasoning behind this
design
so let's talk about the evolution of
yolo now and was voted the people's
choice award people
at cvpr that's a big computer vision
conference
and then later on yellow vision 2 was
later released
and they were introduced batch
normalization which resulted in
improvements in the map score by two
percent and it was also fine-tuned to
work at higher resolutions
this is fairly high resolution for the
computer vision optic detection that is
working in real time i must say
and it gave a four percent increase in
map overall
yolo vision tree now was fine-tuned even
further and introduced multi-scale
training to help better detect smaller
objects
so this concludes our discussion on
yellow i hope you appreciated
what eola has brought to the table
yellow and ssds are basically neck and
neck
in the two best type of object detectors
out there
so now let's take a look at the summary
of what we just learned we learned
basically the evolution
of object detectors from hara cascade
classifiers and hogs
to the first deep learning object
detector which was our cnns then we
introduced fast rcnns and faster rcnns
we then explored single shot detectors
with ssds which are also quite good and
give quite good performance
and then we took a high level overview
of the yellow object detectors
where euler vision 3's latest one and
it's quite good right now
so now let's move on to the next video
where we start taking a look at
implementing object detection with
ssds and yolo in python using opencv 4.
so stay tuned thank you
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