Comparison of Color Classification Using Computer Vision and Deep Neural Network

Mir Rahil; Ravinder Pal Singh

doi:10.55524/ijircst.2022.10.4.20

Abstract

Research on artificial intelligence and machine learning is currently ongoing and is focused on real-world problems. Machine learning is used by computers to make predictions based on the provided data set or existing knowledge. The main goal of our project is to use machine learning to categorize different colors while separating CNN from computer vision. In this work, we used supervised learning to categorize different hues using a binary classification approach. Color detection is the technique of identifying a color. In this scenario, humans can recognize the hue and choose with ease. A computer, however, cannot quickly recognize color. It is challenging to get a computer to quickly detect the color. Given that, we decide to pursue this initiative. Pandas, OpenCV, and the Naive Bayes algorithm are all used in Python. Naive Bayes classifiers are models that assign category labels to issue occurrences that are represented as vectors of feature values, where the category labels are selected from a finite set. There isn't a single method for training these classifiers; rather, there is a family of algorithms built on the premise that, given a category variable, the value of one feature is independent of the value of the other feature. Open-Source Computer Vision Library OpenCV was designed to be computationally effective and with a major emphasis on real-time applications. specialized video encoding for the cloud. Panda may be a platform that runs in the cloud and provides infrastructure for encoding audio and video.

Keywords

Open CV, Colour detection, CNN

References

Brennan Gillis, Bob Kenney, Martin Gillis, Mike Wilkinson, Michelle Adams, Nicole Perry, Carla Hill, Rochelle Owen - Waste Management Practices: Literature Review pg:2
Tran Anh Khoa, Cao Hoang Phuc, Pham Duc Lam, Le Mai Bao Nhu, Nguyen Minh Trong, Nguyen Thi Hoang Phuong, Nguyen Van Dung, Nguyen Tan-Y, Hoang Nam Nguyen, and Dang Ngoc Minh Duc - Waste Management System Using IoT-Based Machine Learning in University
Saurabh Jotwani1, Avesh Sheikh2, Prof. Urvashi Agrawal3, Dr. Narendra Bawane4 - To develop a Garbage detection Drone. Journal of Interdisciplinary Cycle Research Research,441
Anjali Pradipbhai Anadkat, B V Monisha, Manasa Puthineedi, Ankit Kumar Patnaik, Dr. Shekhar R, Riyaz Syed - Drone-based Solid Waste Detection using Deep Learning & Image Processing. Alliance International Conference on Artificial Intelligence and Machine Learning (AICAAM), (2019) 362.
Kellow Pardini, Joel J.P.C. Rodrigues, Ousmane Diallo, Ashok Kumar Das, Victor Hugo C. de Albuquerque, and Sergei A. Kozlov - A Smart Waste Management Solution Geared towards Citizens. Sensors (2020), 20, 2380. Pg: 12 of 15
Parkash, Prabu V - IoT Based Waste Management for Smart City. DOI: 10.15680/IJIRCCE.2016. 04020 Pg: 1267
Praveen Kumar Gupta, Vidhya Shree, Lingayya Hiremath, and Sindhu Rajendran - The Use of Modern Technology in Smart Waste Management and Recycling: Artificial Intelligence and Machine Learning
Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos and Dionisis Kandris - A Review of Machine Learning and IoT in Smart Transportation. Future Internet 11(4) (2019) 94.
Olugboja Adedeji, Zenghui Wang - Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network
M. A. Viraj J. Muthugala, s. M. Bhagya p. Samarakoon, and Mohan Rajesh Elara - Tradeoff Between Area Coverage and Energy Usage of a Self-Reconfigurable Floor Cleaning Robot Based on User Preference
Subham Chakraborty, Sayan Chowdhury, Soumyadip Das, Sayansri Ghosh, Rimona Dutta, Sayan Roy Chaudhuri - Segregable Smart Moving Trash Bin
Bobulski, J., and Kubanek, M - Waste Classification System Using Image Processing and Convolutional Neural Networks. Springer International. (2019)

Cites this article as

M. Rahil, R. P. Singh, "Comparison of Color Classification Using Computer Vision and Deep Neural Network ", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-10, Issue-4, Page No-169-177, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.4.20

Corresponding Author

Mir Rahil

M. Tech Scholar, Department of Electronics and Communication Engineering, RIMT University, Mandi Gobindgarh, Punjab, India

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