Single Shot Detector | SSD | Object Detection Using SSD

Explained what is Single Shot Detector.
You can learn other object detection algorithms from below given link:

If you have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer your queries.

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Single Shot Detector(SSD) – Real Time Object Detection
Object Detection Is A Technique In Computer Vision That Deals With Detecting Examples Of Semantic Targets Of A Specific Class (Eg. Cars, Buildings Or Humans) In Images And Videos. It Is A Technique That Works To Locate And Identify Objects In Digital Images And Videos.

It Specifically Draws Bounding Boxes Around The Object Which Help Us To Locate Where The Objects Are. Many A Times Object Detection Is Mix With Image Recognition.

SSD
There Are Many Object Detection Algorithms In Practice Like R-CNN, Fast R-CNN, Faster R-CNN Etc..

But Single Shot Algorithms More Efficient And Have A Good Accuracy. They Use Deep Learning Based Approaches For Object Detection.

How Single Shot Detection(SSD) Is Different:-
Single Shot Detection – This Means That The Tasks Of Object Localization And Object Classification Are Ready In A Single Forward Pass Of The Network.

Detector – The Network Is A Detector That Also Classifies The Detected Objects.

Single Shot Detector Is Faster Than The Previous State-Of-The-Art Techniques(YOLO) And Is Significantly More Accurate.
SSD Predicts Category Scores And Box Offsets For A Fixed Number Of Default Bounding Boxes Using Convolution Filters Applied To Feature Maps.
To Achieve High Accuracy We Produce Predictions Of Different Scales From Feature Maps Of Different Dimensions, And Then Separate The Predictions By Aspect Ratio.
These Features Lead To High Accuracy, Even On Low Resolution Input Images.
Other Algorithms Normally Use Object Proposal Methodology Where They Would Come Up With A Way To Break Down The Image Segmented Into Parts To Suggest Where They Could Potentially Be Objects. These Algorithms Sacrifice Accuracy.

Therefore Researchers Came Up With An Interesting Solution Where They Do Everything In One Single Shot. It Just Looks At The Image Once, It Doesn’t Have To Go Back To The Image Again, It Doesn’t Have To Run Many Convolutional Neural Networks.

#SingleShotDetector #SSD #ObjectDetection #PifordTechnologies #AI #ArtificialIntelligence #DeepLearning #ConvolutionalNeuralNetwork #CNN #ComputerVision

[村田淳七八]

Thank you for finding the explanation of SSD helpful.May I ask you a simple question? After passing through the layer in the 6-layer model, are there no more convolutional layers?

[neenadkambli]

great video but i had a different doubt in ssd mobilenet why is mobilenet used if ssd can do both detection and classification of objects in image

[sohamroykarmokar]

This is a great video!
Can you make a video on RefineDet: Single-Shot Refinement Neural Network for Object Detection ?

[awaisahmad]

You are best teacher. I have learned a lot from your lectures.

I am also research student and my discipline of research is computer vision.

Thank you so much.

[yontenjamtsho]

Thank you for the excellent video. How SDD produces 8732 BB of different sizes? Is the algorithm generating those boxes?

[yeshwantkumar]

Amazing, Thats great.
I am the one who is the 100th liker of this

[Maria-wmbb]

I think that ssd makes 8732 prediction for each image not for the class.

[aarondurante]

Thank you so much Maam. I am learning a lot. Hope you will create more videos related to this. 👏

[sadekasany]

Thank you ma'am. I can easily understand ur tutarial. So please upload mask rcnn tutarial. Plz ma'am

[hamzanaeem]

You have brilliantly explained but, you need to expand more contents that why we are using these 6 layers, why not 5 etc and what is the function of it, How it extract the features whether they are scale or rotation invariant or not etc etc ?

[last_theorem]

so here we are looking for a bounding box with the highest probablity score for the class and the box . And this is done with the anchor box. Isn't it a less efficient way. Because your object can be of different size and shape so you need bounding boxes of different size and shape so in one way are talking about generating several thousands of bounding boxes and checking the intersection of them it will be a brute force approach. And what if there are boxes with no intersection and .

[MultiZe]

Thank you for the explanation, very helpful video. Do you have references or papers that complement the theoric part of SSD? I'm writting a thesis about CNN and need more theorical info about SSD.

[priyalgeorge]

the explaination is good, but the sound quality is poor

[elektroprogramming]

First, I want to thank you. You make this video, it helps me a lot. I played multiple times.
So, also I have a question
You said that after we have extracted feature map, the next process is SSD that have 8732 in every object.
but in 4:20, you showed me that the image predict with boxes is using the real input image(300x300), not using extracted feauture map .
I need an explaination about it.
Thanks a lot

[hussainasghar]

can you please send the object detection algorithm for SSD please..

[christabelajaero]

thank you for this video. it helped alot. Please can you recommend some papers or article i can read to know more about the theoretical part of SSD?
2. How can i get the SDD Convuluntional neural network (Detector network ) which i should use on a pretrained network, say rasnet-50 network.??? I want to try it on matlab.
Many Thanks again!!!

[thelaughtermedia]

please improve the voice i mean be louder