1. Object Recognition & Detection
An empFinesseTM Fundamentals Solution

2. Regions of Interest (ROI)
Key Terms
Deep Learning
Object Recognition
Convolutional Neural Network (CNN)
Object Detection
Custom Vision API
Regions of Interest (ROI)
Object Classification
MS CNTK

3. Detect like a Human Eye? 04 01 02 03
Locate objects?
Recognize object /text in an image / video?
Memorize and Train?
Recollect / Remember?

4. Sample Use Cases Detect components of a motherboard of any device
Recognize processor type
Recognize missing parts
Recognize individual employees in an office/ODC
Detect in/out of resources just like an human eye
Detect Non-employee vehicles in a parking lot
Recognize a vehicle without any external tags
Trace a car by tracing its entry/exit in each toll
Identify defective stage of a product
Early detection of defects

5. Object Detection Approach

6. About Solution ObjecTell
Process an Image
Extract ROIs
Classify Regions
Faster CNN flow

7. Technology Perspectives
MongoDB
Detection UI
Search UI
Admin UI
CNTK Python scripts
ObjectTell WCF Service
Azure Vision API Connector
DAL Connector
Objects Recognizer
Search Component
Dataset Manager
Metadata Manager
User Query Manager
Resource Data Retriever
Azure Computer Vision API Service
Azure Custom Vision API Service
ObjectTell
Dataset Train UI
Object Marker
Usage Dashboard
Character Recognizer
NLP Core
Video Parser
Usage Analytics
UI Layer
Service Layer
Data Access Layer
Yet to be implemented

8. Current & Future State Products / Libraries Used Maturity Level
MS CNTK 2.3 Library
Anaconda
Python 3.5
MS Custom Vision API
Azure Computer Vision API for OCR
OpenALPR API
Stanford NLP core (yet to integrate)
Maturity Level
First stage: Training / Learning phase (currently got a decent model for cars dataset, grocery dataset)
Second stage: Objects Recognition and Detection– Azure hosted web application is available now. We can improvise this solution to embed within a mobile as an mobile app and user can snap a picture directly through this app and recognize the objects in that picture.
Third stage: Self-learning phase - We have to enhance our solution to self learn based on user corrections/suggestions.
Future State
Recognize other domain / industry related objects
Parse video into individual frames and recognize objects / text
Support English like query through NLP
Embed this feature within a camera
Generate Report on number of detections and accuracy

9. Business Model

10. 38.92 B$ business by 2021 (image Processing)
Business Relevance
38.92 B$ business by 2021 (image Processing)

11. Scalability # Scalability Need Current State Resource Needed 1
Process more images (10,000s) in few hours
It takes around 6-9 hours to process 100+ images
Deep Learning VM / GPU optimized VMs 2
Improve object Recognition and Detection accuracy
Recognizes fully visible objects. If they overlap then accuracy and probability of detecting more objects is 50% 3
Cater to More domains
Trained for Grocery and vehicles dataset, it can be extended to other domains as well
Need VMs/servers with more hard disk space (>500 GB) 4
Cater to more users
Have tested with 5 users – more number of parallel users have impact on detection time
Deep Learning VM / GPU optimized VMs and design level change to create different threads for multiple parallel users 5
Cater to multiple customers at same time
It can cater to 1 customer users per Azure site instance
More HDD space and optimized VMs

12. Snapshots

13. Thank You