Design and application of CNN for emission detection through thermal imagery

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.317203

Keywords:

exhaust emissions, CNN, thermal imagery, motorcycle emissions, air quality, regulatory standards, lambda value

Abstract

Motorcycle exhaust emissions (EE) that do not meet regulatory standards present a significant environmental and public health issue, particularly given the rising number of motorcycles in densely populated areas. These emissions release pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx), which contribute to poor air quality and have adverse effects on human health. Traditional emission testing methods using gas analyzers, while commonly used, face limitations such as sensitivity to environmental fluctuations, the necessity for frequent recalibration, and an intensive testing process requiring specialized expertise. This study addresses these issues by developing an innovative method for emission detection using Convolutional Neural Networks (CNN) applied to thermal images of motorcycle exhausts. The research method involves five key stages: data acquisition, dataset formation, CNN model design and training, model testing, and validation. Thermal images were gathered from 27 motorcycles, representing various brands and engine configurations common in Indonesia, and each image set included 100 samples for both emission-compliant and non-compliant categories. By analyzing thermal patterns, the CNN model was trained to accurately detect combustion patterns indicative of emission status based on the lambda value. This approach enables the model to generalize across different motorcycle models, offering practical adaptability for widespread implementation. The results demonstrate that the CNN model delivers high predictive accuracy, precision, recall, and F1-score, making it a robust tool for assessing motorcycle emission compliance. This CNN-based approach provides a practical solution for real-time, large-scale emission monitoring and regulatory enforcement, reducing dependency on conventional methods. Its scalability and adaptability position it as a valuable advancement in emission monitoring technology, with significant potential for supporting environmental standards and improving air quality management

Author Biographies

Doddi Yuniardi, Gunadarma University

Master of Electronic Engineering, Lecturer

Department of Mechanical Engineering

Advanced Mechanical Engineering Laboratory Staff

Sarifuddin Madenda, Gunadarma University

Professor of Information Technology, Lecturer, Director of Program

Department of Informatics Engineering

Information Technology Doctoral Program

Ridwan Ridwan, Gunadarma University

Professor of Mechanical Engineering, Lecturer

Department of Mechanical Engineering

Prihandoko Prihandoko, Gunadarma University

Doctor of Information Technology, Lecturer

Department of Information Technology

Abdul Azis Abdillah, University of Birmingham; Politeknik Negeri Jakarta

PhD Candidate

CASE Automotive Research Centre

Department of Mechanical Engineering

Lecturer

Department of Mechanical Engineering

Sulaksana Permana, Gunadarma University; Universitas Indonesia

Doctor of Engineering in Metallurgy and Materials, Lecturer

Department of Mechanical Engineering

Laboratory of Prof. Dr. Ir. Johny Wahyuadi S., DEA

Department of Metallurgy and Materials

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Design and application of CNN for emission detection through thermal imagery

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Published

2024-12-27

How to Cite

Yuniardi, D., Madenda, S., Ridwan, R., Prihandoko, P., Abdillah, A. A., & Permana, S. (2024). Design and application of CNN for emission detection through thermal imagery. Eastern-European Journal of Enterprise Technologies, 6(10 (132), 6–18. https://doi.org/10.15587/1729-4061.2024.317203

Issue

Vol. 6 No. 10 (132) (2024): Ecology

Section

Ecology

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Copyright (c) 2024 Doddi Yuniardi, Sarifuddin Madenda, Ridwan Ridwan, Prihandoko Prihandoko, Abdul Azis Abdillah, Sulaksana Permana

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