MultimodalGasData: Multimodal Dataset for Gas Detection and Classification

Narkhede, Parag; Walambe, Rahee; Chandel, Pulkit; Mandaokar, Shruti; Kotecha, Ketan

doi:10.3390/data7080112

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…This study makes use of the multimodal gas detection dataset in [74], which represents multimodal data produced by modern smart applications. To identify the different types of gases and determine their concentrations, A system with a thermal camera and gas sensors is used to collect the gas measurements.…”

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

“…To identify the different types of gases and determine their concentrations, A system with a thermal camera and gas sensors is used to collect the gas measurements. The unit used to acquire the dataset employed in this article can be found in [74]. A thermal camera is a tool that uses IR light to measure temperature fluctuations.…”

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

“…Various sensor properties, such as sensitivity, selectivity, detection limit, reaction time, etc., affect the gas sensor's performance. The paper [74] contains information about the gas sensors utilized and their sensitivity to different gases. The gas sensing devices were separated from one another by 1 mm throughout the data collection procedure.…”

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

“…Table 1. Examples of data from measurements made by gas sensors and their related thermal pictures [74]. The measurements from the gas sensors are MQ2, MQ3, MQ5, MQ6, MQ7, MQ8, and MQ135 in that order.…”

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

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Multitask Deep Learning-Based Pipeline for Gas Leakage Detection via E-Nose and Thermal Imaging Multimodal Fusion

Attallah

2023

Chemosensors

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Innovative engineering solutions that are efficient, quick, and simple to use are crucial given the rapid industrialization and technology breakthroughs in Industry 5.0. One of the areas receiving attention is the rise in gas leakage accidents at coal mines, chemical companies, and home appliances. To prevent harm to both the environment and human lives, rapid and automated detection and identification of the gas type is necessary. Most of the previous studies used a single mode of data to perform the detection process. However, instead of using a single source/mode, multimodal sensor fusion offers more accurate results. Furthermore, the majority used individual feature extraction approaches that extract either spatial or temporal information. This paper proposes a deep learning-based (DL) pipeline to combine multimodal data acquired via infrared (IR) thermal imaging and an array of seven metal oxide semiconductor (MOX) sensors forming an electronic nose (E-nose). The proposed pipeline is based on three convolutional neural networks (CNNs) models for feature extraction and bidirectional long-short memory (Bi-LSTM) for gas detection. Two multimodal data fusion approaches are used, including intermediate and multitask fusion. Discrete wavelet transform (DWT) is utilized in the intermediate fusion to combine the spatial features extracted from each CNN, providing spectral–temporal representation. In contrast, in multitask fusion, the discrete cosine transform (DCT) is used to merge all of the features obtained from the three CNNs trained with the multimodal data. The results show that the proposed fusion approach has boosted the gas detection performance reaching an accuracy of 98.47% and 99.25% for intermediate and multitask fusion, respectively. These results indicate that multitask fusion is superior to intermediate fusion. Therefore, the proposed system is capable of detecting gas leakage accurately and could be used in industrial applications.

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Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

Section: Multimodal Dataset For Gas Leakage Detectionmentioning

confidence: 99%

See 2 more Smart Citations