YOLO: Unified, Real-Time Object Detection · Ren, Shaoqing, et al. "Faster R-CNN: towards real-time...

Anup Deshmukh, Pratheeksha Nair Seminar March 28, 2018 1 / 25

YOLO: Unified, Real-Time Object DetectionJoseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi

Anup Deshmukh, Pratheeksha Nair

March 28, 2018

Outline

1 Discussion leading towards YOLO!

2 What is YOLO?IntroductionUnified Object DetectionThe design of cost function

3 Experimental analysisComparisons

4 Limitations of YOLO

5 Summary


The Infographic!

Powered by TCPDF (www.tcpdf.org)


Little bit of History

• Object Detection using Hog Features (2005)

• Region-based Convolutional Neural Networks(R-CNN) (Oct2014)

• Fast(R-CNN) (Sept 2015)

• Faster(R-CNN) (Jan 2016)



Next Subsection





5 Summary



Introduction

• Modelling the Regression problem

• Using bounding boxes with associated class prob.

• Single DNN - end to end optimization

• Full image as input (Understanding of context)

• Extremely fast (45fps)



Next Subsection





5 Summary



Unified Object Detection

• YOLO sees entire image during both training and testing

• Single regression problem (image pixels to bounding boxes andclasses)



Unified Object Detection - Workflow

• YOLO divides input image into SxS grid

• If the center of an object falls inside a grid cell, that grid cell isresponsible for detecting the object




• Each grid cell predicts B bounding boxes and confidence scoresfor these boxes

• Confidence of box being accurate and box containing an object

Confidence = Pr(object) * IOUtruthpred

• Each bounding box is represented as [x , y , w , h, conf ]




• Each grid cell calculates Ci = Pr(Classi |Object) for all classes ofobjects

• In each grid cell, one box is chosen and Ci is calculated

• This is encoded as a tensor of size [S x S x (B*5 + C)]



Unified Object Detection - Testing• The conditional class probabilities and confidence of predicted

bounding boxes are multiplied

Pr(Classi |Object) ∗ Pr(Object) ∗ IOUtruthpred = Pr(Classi ) ∗ IOUtruth

pred

• For each predicted box, class specific confidence scores• Confidence of predicted box fitting the object and object

belonging to that class



Quick overview of YOLO



Next Subsection





5 Summary



The design

• The authors have used sum-squared error because it is easy tooptimize.

• Sum-squared loss should not equally weight errors with objectequally with error when there is no object.

• Sum-squared loss also should not equally weight errors in largeboxes and small boxes.



Cost function

Term 1

Term 2



Next Subsection





5 Summary



DPM - The only other model that runs in realtime

Table:

DPM YOLO

processes 30fps processes 45fpsmAP is 26.1% mAP is 63.4%



R-CNN’s

Table:

RCNN YOLO

processes 5-18fps processes 45fps3x times better at localizing lags in localizing accuracyfar more background errors analyzing image as a whole



The Generalization

• PICASSO and People-Art Dataset

• YOLO generalizes better to new domains better than others

• RCNN drops considerably (Selective search is tuned only fornatural images)

• YOLO has good performance (models shape, size andrelationship between objects and their locations)



Limitations

• Errors are treated same in small bounding boxes versus largebounding boxes.

• Small objects that appear in groups



Summary

YOLO is modelled as a super-fast regression problem which takes aninput image and learns the class probabilities and bounding box

coordinates.



References

• Girshick, Ross, et al. ”Rich feature hierarchies for accurate objectdetection and semantic segmentation.” Proceedings of the IEEEconference on computer vision and pattern recognition. 2014.

• Girshick, Ross. ”Fast r-cnn.” arXiv preprint arXiv:1504.08083(2015).

• Ren, Shaoqing, et al. ”Faster R-CNN: towards real-time objectdetection with region proposal networks.” IEEE transactions onpattern analysis and machine intelligence 39.6 (2017):1137-1149.

• Redmon, Joseph, et al. ”You only look once: Unified, real-timeobject detection.” Proceedings of the IEEE conference oncomputer vision and pattern recognition. 2016.

• The Infographics: https://medium.com/@nikasa1889/the-modern-history-of-object-recognition-infographic-aea18517c318



Questions/Comments?



Thank You!



YOLO: Unified, Real-Time Object Detection · Ren, Shaoqing, et al. "Faster R-CNN: towards real-time...

Documents

Transcript of YOLO: Unified, Real-Time Object Detection · Ren, Shaoqing, et al. "Faster R-CNN: towards real-time...