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1. Object Detection using
Deep Learning
By:- Vikul Kumar(2011CS24)

2. Outline
▸ Introduction
▸ Objective
▸ History
▸ Deeplearing model for object detection
▸ Paper Review
▸ Result
▸ Comperison
▸ conclusion
▸ Reference
2

3. Introduction
▸ Object detection to recognize and detect different objects present in an
image or video and label them to classify these objects.
▸ Object detection is a significant research area in Computer Vision.
▸ Invention and Evolution of Deep learning have changed the traditional ways
of object detection and reorganization system.
▸ Deep learning methods are the strongest method for object detection.
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4. ▸ Object detection helps in the recognition, detection, and localization of
multiple visual instances of objects in an image or a video.
▸ It can be used to count the number of instances of unique objects and mark
their precise locations, along with labeling.
▸ It identifies the feature of Images rather than traditional object detection
methods and generates an intelligent understanding of images just like human
vision works.
▸ Object Detection is used to identify the location of the object in an image, Face
detection, medical imaging, Driverless cars, security, surveillance, machine
inspection etc.
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Objective

5. ▸ In the last 20 years, the progress of object
detection has generally gone through two
significant development periods, starting
from the early 2000s:
▸ 1. Traditional object detection- the early 2000s
to 2014.
▸ 2. Deep learning-based detection- after 2014.
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History

6. 6
Deeplearing model for object detection
1. R-CNN model family: It stands for Region-based Convolutional
Neural Networks
R-CNN
Fast R-CNN
Faster R-CNN
2. SSD: SSD (Single Shot MultiBox Detector)
3. YOLO model family: It stands for You Look Only Once
YOLOv1
YOLOv2
YOLOv3

7. 7
Structure
RCNN: Regions with CNN features
Architecture Fast – RCNN
Faster R-CNN Structure

8. ▸ Name- A Survey of Deep Learning-Based Object Detection
▸ (Conference:- IEEE: September 5, 2019)
▸ Author:- FAN ZHANG, LINGLING LI, RONG QU(Member, IEEE) ,
▸ A variety of object detection methods in a systematic manner,
covering the one-stage and two-stage detectors.
▸ Architecture of exploiting object detection methods to build an
effective and efcient system and point out a set of development
trends to better follow the state-of-the-art algorithms.
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Paper Review
Paper-1

9. ▸ Name- YOLOv4 :Optimal SpeedandAccuracyofObjectDetection
▸ Author:- Alexey,Chien WangandYungMark Liao
(Institute ofInformationScienceAcademia SinicaTaiwan)
▸ Used Dataset:- MS COCO Dataset
▸ Comparisonofproposed YOLOv4 andother
state ofartobjectdetector.
▸ YOLOv4 runstwicefasterthan with
EfficientDet withcomparable perform
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Paper-2

10. ▸ Name- object Detection using Deep Learning
▸ (Conference:- International Research Journal of Engineering and
Technology (IRJET) : 10 | Oct 2019 )
▸ Author:- Prof. Pramila M. Chawan(Associate Professor, Dept. of Computer
Engineering and IT, VJTI College, Mumbai, Maharashtra, India ),
Shubham Pal(M.Tech Student, Dept. of Computer Engineering and IT,
VJTI College, Mumbai, Maharashtra, India )
▸ Object detection framework like Convolutional Neural Network(CNN),
Recurrent neural network (RNN), faster RNN, You only look once (YOLO).
▸ Proposed method gives the correct result with accuracy.
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Paper-3

11. 11
Result: Theproposedapproach produced Sensitivity 92.14%,Specificity 91.24%and
Accuracy90.88%.

12. 12
Comperison
R-CNN
Fast-
RCNN
Faster-
RCNN
Test time
per image
50
seconds
2 seconds
0.2
seconds
Speed 1x 25x 250x
Comparison of test-time speed of object
detection algorithms

13. ▸ Computer vision task that refers to the process of locating and
identifying multiple objects in an image.
▸ Deep learning algorithms like YOLO, SSD and R-CNN detect objects on
an image using deep convolutional neural networks,
▸ Deep convolutional neural networks are the most popular class of deep
learning algorithms for object detection.
▸ These networks can detect objects with much more efficiency and
accuracy than previous methods.
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Conclusion

14. ▸ Real-time object Detection using Deep Learning: A survey (Prof. Pramila M.
Chawan,Shubham Pal, (IRJET) )
▸ Feature Selection Module for CNN Based Object Detector (YONGJUN MAAND
SONGHUAZHANG)
▸ Moving Object Detection Using Convolutional Neural Networks (Shraddha Mane
and Prof.Supriya Mangale )
▸ YOLOv4: Optimal Speed and Accuracy of Object Detection (Alexey
Bochkovskiy, Chien-Yao Wang and Hong-Yuan Mark Liao)
▸ Artificial Intelligence in Object Detection(Ashish Kumar,Department of
Electrical Engineering and Computer Science,National Taipei University of
Technology)
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Reference

15. 15
THANKS!