Floor detection system which will detect the quality of Floor, identify whether its clean or unclean and then recommend the steps to take after identifying the property of the Floor.
Aim and Objectives
Aim
To create a Floor detection system which will detect the quality of Floor, identify whether its clean or unclean and then recommend the steps to take after identifying the property of the Floor.
Objectives
• The main objective of the project is to create a program which can be either run on Jetson nano or any pc with YOLOv5 installed and start detecting using the camera module on the device.
• Using appropriate datasets for recognizing and interpreting data using machine learning.
• To show on the optical viewfinder of the camera module whether a Floor is clean or unclean.
Abstract
• A Floor’s cleanliness can be detected by the live feed derived from the system’s camera.
• We have completed this project on jetson nano which is a very small computational device.
• A lot of research is being conducted in the field of Computer Vision and Machine Learning (ML), where machines are trained to identify various objects from one another. Machine Learning provides various techniques through which various objects can be detected.
• One such technique is to use YOLOv5 with Roboflow model , which generates a small size trained model and makes ML integration easier.
• Clean and Beautiful looking floors provide a refreshing and mesmerizing look and helps in creating a good ambience in a given environment.
• Clean floors provides traction improving the safety of the place by eliminating slipping and also removes allergens to further provide with a fresher and healthier environment.
Introduction
• This project is based on a Floor detection model with modifications. We are going to implement this project with Machine Learning and this project can be even run on jetson nano which we have done.
• This project can also be used to gather information about Floor condition, i.e., Clean, Unclean.
• Floor can be classified into clean, unclean, clear, dirty, spotless etc based on the image annotation we give in roboflow.
• Floor detection in our model sometimes becomes difficult because of various textures in floor like spots texture, lines texture, or various other graphical textures. However, training our model with the images of these textured floor makes the model more accurate.
• Neural networks and machine learning have been used for these tasks and have obtained good results.
• Machine learning algorithms have proven to be very useful in pattern recognition and classification, and hence can be used for Floor detection as well.
Literature Review
• This project is based on a Floor detection model with modifications. We are going to implement this project with Machine Learning and this project can be even run on jetson nano which we have done.
• This project can also be used to gather information about Floor condition, i.e., Clean, Unclean.
• Floor can be classified into clean, unclean, clear, dirty, spotless etc based on the image annotation we give in roboflow.
• Floor detection in our model sometimes becomes difficult because of various textures in floor like spots texture, lines texture, or various other graphical textures. However, training our model with the images of these textured floor makes the model more accurate.
• Neural networks and machine learning have been used for these tasks and have obtained good results.
• Machine learning algorithms have proven to be very useful in pattern recognition and classification, and hence can be used for Floor detection as well.
Jetson Nano Compatibility
• The power of modern AI is now available for makers, learners, and embedded developers everywhere.
• NVIDIA® Jetson Nano™ Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.
• Hence due to ease of process as well as reduced cost of implementation we have used Jetson nano for model detection and training.
• NVIDIA JetPack SDK is the most comprehensive solution for building end-to-end accelerated AI applications. All Jetson modules and developer kits are supported by JetPack SDK.
• In our model we have used JetPack version 4.6 which is the latest production release and supports all Jetson modules.
Proposed System
- Study basics of machine learning and image recognition.
- Start with implementation
Front-end development Back-end development- Testing, analysing and improvising the model. An application using python and Roboflow and its machine learning libraries will be using machine learning to identify the cleanliness of Floor.
- Use datasets to interpret the Floor and suggest whether the Floor are clean or unclean.
Methodology
The floor detection system is a program that focuses on implementing real time floor detection. It is a prototype of a new product that comprises of the main module: Floor detection and then showing on viewfinder whether clean or unclean. Floor Detection Module
This Module is divided into two parts:
1] Floor detection
• Ability to detect the location of floor in any input image or frame. The output is the bounding box coordinates on the detected floor.
• For this task, initially the Dataset library Kaggle was considered. But integrating it was a complex task so then we just downloaded the images from gettyimages.ae and google images and made our own dataset.
• This Datasets identifies floor in a Bitmap graphic object and returns the bounding box image with annotation of floor present in a given image.
2] Cleanliness Detection
• Classification of the floor based on whether it is clean or unclean.
• Hence YOLOv5 which is a model library from roboflow for image classification and vision was used.
• There are other models as well but YOLOv5 is smaller and generally easier to use in production. Given it is natively implemented in PyTorch (rather than Darknet), modifying the architecture and exporting and deployment to many environments is straightforward.
• YOLOv5 was used to train and test our model for various classes like clean, unclean. We trained it for 149 epochs and achieved an accuracy of approximately 93%.
Jetson Nano 2GB Developer Kit.
Setup
Advantages
➢ The Floor detection system will be of great advantage where a user has lack of time, motivation, unwell or differently abled.
➢ It will be useful to users who are very busy because of work or are because of prior schedules.
➢ Just place the viewfinder showing the Floor on screen and it will detect it.
➢ It will be faster to just then clean floor using minimal or very less workforce.
Application
➢ Detects Floor clarity in a given image frame or viewfinder using a camera module.
➢ Can be used to clean Floor when used with proper hardware like machines which can clean.
➢ Can be used as a reference for other ai models based on floor detection
Future Scope
➢ As we know technology is marching towards automation, so this project is one of the step towards automation.
➢ Thus, for more accurate results it needs to be trained for more images, and for a greater number of epochs.
➢ Cleaning floors inside vehicles, trains, buses automatically as well as outer surfaces of ships and submarines can be considered a good use of our model.
Conclusion
➢ In this project our model is trying to detect floors for whether they are clean or unclean and then showing it on viewfinder live as what the state of floor is.
➢ This model solves the basic need of having a clean and clear floor for our users who because of lack of time or other reasons are unable to keep their floor clean.
➢ It can even ease the work of people who are in the sanitization industry or the cleaning industry and save them a lot of time and money.
Refrences
1]Roboflow :- https://roboflow.com/
2] Datasets or images used: https://www.gettyimages.ae/search/2/image?family=creative&phrase=floor
3] Google images
Articles
[2]https://shinycarpetcleaning.com/benefits-of-having-floor-cleaning-maintenance/