Abstract
Gas leaks can cause sever air contamination and are potentially harm to human health and the environment. The use of mobile robot technology in areas where gas leakage often occur, can reduce the risk of harm to human workers. Mobile robots can be equipped with accurate and precise gas sensors to provide information on the position of gas sources. However, the problem in detecting the basis of a gas leak is the presence of outside airflow, which causes dispersion this, can make gas detection difficult because it causes the gas concentration to decrease at the position of the leak source. In this research, a mobile robot was developed to determine the location of the origin of gas leaks using a thermal imaging camera and the YoloV5 algorithm. YoloV5 is a deep learning algorithm based on Convolutional Neural Network (CNN) to perform object detection in one step. The results of experiments show that the mobile robot is able to accurately find the location of the gas leak with a success rate of 91.25%.
| Original language | English |
|---|---|
| Title of host publication | Proceeding - IEEE 9th Information Technology International Seminar, ITIS 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350306835 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 9th IEEE Information Technology International Seminar, ITIS 2023 - Batu Malang, Indonesia Duration: 18 Oct 2023 → 20 Oct 2023 |
Publication series
| Name | Proceeding - IEEE 9th Information Technology International Seminar, ITIS 2023 |
|---|
Conference
| Conference | 9th IEEE Information Technology International Seminar, ITIS 2023 |
|---|---|
| Country/Territory | Indonesia |
| City | Batu Malang |
| Period | 18/10/23 → 20/10/23 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- CNN
- Mobile robot
- YOLOV5
- air contamination
- thermal camera
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