Computer Vision for Robots in Production Line

One of the main challenges in the factory digitization is collecting different types of data from existing, aged and heterogeneous machines in the factory floor. Gadgeon’s flagship product provides a way out to this.

Results / Outcomes

Deep learning was applied to the image and text recognition problem which gave very high accuracy than the traditional methods.
Custom dataset based trained neural network for text recognition.
A real time machine vision solution that can process live video feeds and provide directions to the robot in a manufacturing line.
An image recognition module that can be easily trained with less sample images and less processing power.
Custom image processing methods for user defined tasks.

System Description

Technology used :

TensorFlow, ROS, OpenCV, Robotic Arm, Convolutional Neural Networks, OCR

Challenge

Fast and accurate image recognition and localization feature for robot in testing infotainment systems.
Text recognition that supports graphical backgrounds, dynamic text, varying fonts, warped text in real time.
Detecting on-screen movements and changes on the display of DUT.
Fine tuning the software to the particular user model for high accuracy.
Providing positional feedback, warnings and device status to the robotic system.

Text Recognition Module

Data preparation, training, neural network customization
Convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based text recognition using tools such as OpenCV, Tensorflow

Image Recognition Module

Built a real time classification program by using off the shelf neural network architecture
MobileNet, InceptionV3 architecture based CNN models, Tensorflow, OpenCV