Image recognition in practice

The possibilities of using image recognition technologies to solve real world problems are immense. Naming just a few examples, image recognition is used for automated camera focus, automates the attendance taking procedure in many colleges and it assists the police in investigations on people. In the manufacturing industry, image recognition can even be used to check for production defects. Generally speaking, image recognition technologies can complete relatively simple, repetitive tasks humans have to do so far.

Also, technology trends point to a prosperous image recognition future. Cameras with integrated face recognition as well as technologies like Google's reverse image search or image classification, Apple’s person identification, or object recognition as well as other machine learning models are gradually turning into a commodity in our daily lives.

Naturally, those capabilities are valuable levers for the use in businesses as well. As an example, the Deloitte Analytics Institute used the technology of image recognition to help an automotive client automate the inspection of rental cars at return. Images of the car are fed into a model to automatically detect damages. This highly accelerates the process of returning the car after a rental period. The main business goal is to reduce the amount of specialists staying at every return station in two to three shifts for inspection and valuation purposes. Within the scope of the project, the Deloitte Analytics Institute successfully deployed a Convolutional Neural Network (CNN) for damage detection.

The Image Recognition Market is Estimated to grow at a CAGR of 19.5% to USD 38.92 Billion by 2021 offering vast landscape for business applications.

Image Recognition Market – Global Forecast 

Image recognition is a valuable technology which might also facilitate the automation of your business processes. Nevertheless this “tool” needs to be built as combination of various libraries and technologies in order to provide an image recognition application.
We suggest you ask yourself the following questions:

• Do you have use case opportunities for image recognition, e.g. in the field of Quality Assurance, Quality Management or Damage Detection? 
• Are those processes currently analyzed by humans primarily?
• Is visual sensor data already created along business or technological processes?