computer vision based object sorter

The industries are at the verge of the fourth technological revolution formally termed as Industry 4.0 that would implement industrial scale mechanisms via smart methodologies such as IoT, IoS, machine Learning, Computer Vision etc.in a more efficient, reliable and optimum manner. The overwhe

Project Title

Project Area of Specialization

Mechatronics Engineering

Project Summary

Vision etc.in a more efficient, reliable and optimum manner. The overwhelming number of computers: envisaged

from Moore’s law and the smart methodologies mentioned above which are top notch technologies until now

which date back to the “Summer Vision Project” of MIT elevates the potential of the whole industrial automation process, therefore, the task that were done manually and required time and human effort are now accomplished at an accelerated rate, with little or no human assistance, thus giving rise to a smarter environment

Project Objectives

The project aims to develop a model that is streamlined with industry 4.0, it will be compatible with the forthcoming industrial automation era. The project will use Computer Vision coupled with Machine Learning: the former provides the means to detect, separate and subsequently sort the objects and latter eases the learning process, as a result,

we don’t have to spoon feed every instruction to the machine, but instead we rely on Neural networks that are easier to implement, better performing and are not prone to inaccuracies and shortcomings. The proposed model will

be automated to the full extent, it will interface Raspberry pi, hence facilitating the communication between hardware model and software system and the result will be increased maneuverability of the model. The model also aims

to provide a sorting system that will be easier to interface with other industrial operations and consequently will be able to synchronize itself with rest of the industry.

Project Implementation Method

Project Implementation Method:

The implementation of the project is divided into three phases stated below:

1.Devolping Hardware Model: the first stage of the project is crafting a hardware model that automatically feeds

15-20 industrial objects (Lego bricks are considered as standard industrial objects in developmental phase) via

feeder from one end of the model, the objects traverse through feeder, they are separated by

feeder, conveyer belt takes a single object and the raspberry pi will perform sorting mechanism and the

object will be subsequently sorted at another end. The Raspberry pi: the former is responsible for controlling Servo motor of the feeder, DC gear motor of conveyer belt and later captures the image of the Lego brick, the image is processed by the neural network to identify whether the image contains a Lego brick or not, once it is identified as a Lego brick, the image is transferred to a complex neural network stored in the Linux server to process the image and compute to which brick type it belongs and after this computation the Raspberry Pi categorizes the object or anything like brick to its respective sorted bin.

2.The Training Phase: once the hardware model of the prototype is ready the second of the project begins; as

stated earlier the project uses Machine Learning which require an adequate amount of data to train itself for

accurate predictions, so we will provide enough datasets to the model that it can assess Lego brick from

different positions, orientations, angles etc. therefore, the model will no longer be susceptible to the

shortcomings that were posed to the previous models and thwarted the whole automation process.

The training phase can be further divided into two substages mentioned below:

2.1 Identifying Lego brick: The first and foremost part is to identify the Lego brick with the help of Computer

Vision techniques that are: capturing the image, processing it on some Neural network and yielding some output

which triggers the further mechanism stated in 2.2.

2.2 Categorizing the Lego brick: once the Lego brick is detected the image of that brick will be transferred to the

neural network (stored in google cloud-based server) that after processing accurately sort the object to its

respective bin. The model in the training phase will be experimented with different neural networks such as

CNN, RCNN, or a combination of different neural networks to yield the best results.

3.The Testing Phase: after the training we will test our model that will be based on industrial metrics such as

separating accuracy, sorting accuracy etc. Moreover, that results will suggest if the model needs to be trained

further or not.

Project Implementation Method: