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seminar ppt.pptx

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seminar ppt.pptx

  1. 1. Object Detection using Deep Learning By:- Vikul Kumar(2011CS24)
  2. 2. Outline ▸ Introduction ▸ Objective ▸ History ▸ Deeplearing model for object detection ▸ Paper Review ▸ Result ▸ Comperison ▸ conclusion ▸ Reference 2
  3. 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. 3
  4. 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. 4 Objective
  5. 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. 5 History
  6. 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. 7 Structure RCNN: Regions with CNN features Architecture Fast – RCNN Faster R-CNN Structure
  8. 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. 8 Paper Review Paper-1
  9. 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 9 Paper-2
  10. 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. 10 Paper-3
  11. 11. 11 Result: Theproposedapproach produced Sensitivity 92.14%,Specificity 91.24%and Accuracy90.88%.
  12. 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. 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. 13 Conclusion
  14. 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) 14 Reference
  15. 15. 15 THANKS!

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