Faster YOLO: An Accurate and Faster Object Detection Method
1. 1
Topic : Faster YOLO : An Accurate and faster Object Detection Method
Guide: Prof. Deepali Hajare
Subject : Seminar and Technical Communication
Name of Student : Sanjay Prajapati
Roll Number : 76
AY 2022-23 Semester V
Third Year Engineering
Department of Artificial Intelligence and Data Science
Dr. D. Y. Patil Institute of Engineering Management and Research
3. Abstract
• In the computer vision, object detection has always been considered one of the most challenging issues
because it requires classifying and locating objects in the same scene.
• Many object detection approaches were recently proposed based on deep convolutional neural networks
which have been demonstrated to achieve outstanding object detection performance compared to other
approaches.
• So in this paper the new method called Faster-Yolo which is able to perform the real time object
detection is been discussed.
4. Introduction
• In recent several years, deep learning has been actively applied in various fields of computer vision, like
image classification , object detection, etc.
• As one of the most challenging issues in the computer vision is Object detection.
• The purpose of object detection is to locate different objects in the same scene and designates labels to
the bounding boxes of the objects .
• Two main problems need to be solved in object detection: where is the object in the image (namely
location problem) and what is the object (namely category problem) .
5. Introduction
• Therefore, we can think of the object detector as a fusion of object location finder and object
recognition.
• With the development of deep neural networks specially convolutional neural network (CNN) and R-
CNN object detection has reached an impressive improvement.
• But they have some drawbacks when come to faster computation and processing the result.
• So here comes YOLO into the picture , which treats the object detection task as single problem which
relieves the computational complexity .
• The idea behind YOLO is that to use the entire image as the input to the network and directly return
bounding boxes coordinates in the output layer. Which Provides the yolo a significant advantage over
faster R-CNN .
6. Methodology
• The YOLO algorithm works by dividing the image into N grids, each having an equal dimensional region of SxS.
Each of these N grids is responsible for the detection and localization of the object it contains.
• Correspondingly, these grids predict B bounding box coordinates relative to their cell coordinates, along with the
object label and probability of the object being present in the cell.
• This process greatly lowers the computation as both detection and recognition are handled by cells from the image.
9. Methodology
• But , It brings forth a lot of duplicate predictions due to multiple cells predicting the same object with
different bounding box predictions.
• YOLO makes use of Non Maximal Suppression to deal with this issue.
• In Non Maximal Suppression, YOLO suppresses all bounding boxes that have lower probability scores.
10. Real-world Applications of the YOLO
Algorithm
• Autonomous Driving - Companies like Google & Tesla use computer vision cameras for improving
their self driving cars.
• Security and Surveillance – Automation of inventory management, analysing customer footfall and
security for theft are the major applications.
• Healthcare – Computer vision is being used to help diagnose/predict health conditions like cancer in
early stages.
• Agriculture – Precision farming and early detection of crop diseases to optimize yield .
• Manufacturing Industries – Detecting manufacturing defects not visible to human eyes.
11. Future Scope
• Object detection is a key ability for most computer and robot vision system.
• Although great progress has been observed in the last years, and some existing techniques are now part of
many consumer electronics or have been integrated in assistant driving technologies.
• We are still far from achieving human-level performance, in particular in terms of open-world learning.
• Finally, we need to consider that we need object detection systems for nano-robots or for robots that will
explore areas that have not been seen by humans
• In such cases, a real-time open-world learning ability will be critical.
12. Conclusion
YOLO is the object detection algorithm as it is much faster compared to other
algorithms while being able to maintain a good accuracy.
13. References
• Faster-YOLO: An accurate and faster object detection method
https://www.sciencedirect.com/science/article/abs/pii/S1051200420301
019?via%3Dihub , 2020.
• Andrew NG , CNN : Deeplearning.ai , Coursera.org , 2017
• Sainagesh Veeravali , Object Detection and identification ,
https://www.researchgate.net/publication/337464355_OBJECT_DETE
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