A Lightweight Multi-Modal Fusion Framework for In-Vehicle Occupant Detection on Embedded Systems.
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| Title: | A Lightweight Multi-Modal Fusion Framework for In-Vehicle Occupant Detection on Embedded Systems. |
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| Authors: | Wu, Enqi1 wuenqi@usst.edu.cn, Liu, Qi2 15907957016@163.com, Pan, Yizhou2 1944520474@qq.com, Kou, Jiaming2 bzmh007@163.com, Duan, Hanzuo2 a1245481661@qq.com |
| Source: | IAENG International Journal of Computer Science. Aug2026, Vol. 53 Issue 8, p3154-3162. 9p. |
| Subjects: | Embedded computer systems, Automobile safety, Human facial recognition software, Object recognition (Computer vision), Multisensor data fusion |
| Abstract: | The unintentional abandonment of children in locked vehicles is a critical public safety issue often leading to fatal vehicular heatstroke. To mitigate this risk, this study presents a resource-efficient, multi-modal detection framework designed for embedded automotive platforms. Unlike traditional sensor-based methods prone to environmental noise, or heavy deep-learning models unsuitable for edge deployment, the proposed system integrates lightweight face classification with robust human body detection. Specifically, a cascaded architecture combines MTCNN for face localization with a structurally pruned Convolutional Neural Network (CNN) for age classification (child vs. adult). The pruning strategy reduces computational complexity by 73.7% while maintaining 99.4% accuracy, ensuring low-latency processing. For body detection, an optimized Single Shot MultiBox Detector (SSD) with a ResNet50 backbone is enhanced by Multi-scale Feature Fusion and an Efficient Channel Attention (ECA) mechanism to address occlusion challenges in vehicle cabins. Experimental results on the NVIDIA Jetson Nano platform demonstrate that the system achieves an Average Precision (AP) of 78.16% and operates at 16 FPS. This balance of high accuracy and computational efficiency provides a practical, real-time solution for preventing left-behind occupant incidents in intelligent vehicles. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195911346 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Lightweight Multi-Modal Fusion Framework for In-Vehicle Occupant Detection on Embedded Systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Enqi%22">Wu, Enqi</searchLink><relatesTo>1</relatesTo><i> wuenqi@usst.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qi%22">Liu, Qi</searchLink><relatesTo>2</relatesTo><i> 15907957016@163.com</i><br /><searchLink fieldCode="AR" term="%22Pan%2C+Yizhou%22">Pan, Yizhou</searchLink><relatesTo>2</relatesTo><i> 1944520474@qq.com</i><br /><searchLink fieldCode="AR" term="%22Kou%2C+Jiaming%22">Kou, Jiaming</searchLink><relatesTo>2</relatesTo><i> bzmh007@163.com</i><br /><searchLink fieldCode="AR" term="%22Duan%2C+Hanzuo%22">Duan, Hanzuo</searchLink><relatesTo>2</relatesTo><i> a1245481661@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Aug2026, Vol. 53 Issue 8, p3154-3162. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Embedded+computer+systems%22">Embedded computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Automobile+safety%22">Automobile safety</searchLink><br /><searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The unintentional abandonment of children in locked vehicles is a critical public safety issue often leading to fatal vehicular heatstroke. To mitigate this risk, this study presents a resource-efficient, multi-modal detection framework designed for embedded automotive platforms. Unlike traditional sensor-based methods prone to environmental noise, or heavy deep-learning models unsuitable for edge deployment, the proposed system integrates lightweight face classification with robust human body detection. Specifically, a cascaded architecture combines MTCNN for face localization with a structurally pruned Convolutional Neural Network (CNN) for age classification (child vs. adult). The pruning strategy reduces computational complexity by 73.7% while maintaining 99.4% accuracy, ensuring low-latency processing. For body detection, an optimized Single Shot MultiBox Detector (SSD) with a ResNet50 backbone is enhanced by Multi-scale Feature Fusion and an Efficient Channel Attention (ECA) mechanism to address occlusion challenges in vehicle cabins. Experimental results on the NVIDIA Jetson Nano platform demonstrate that the system achieves an Average Precision (AP) of 78.16% and operates at 16 FPS. This balance of high accuracy and computational efficiency provides a practical, real-time solution for preventing left-behind occupant incidents in intelligent vehicles. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 3154 Subjects: – SubjectFull: Embedded computer systems Type: general – SubjectFull: Automobile safety Type: general – SubjectFull: Human facial recognition software Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Multisensor data fusion Type: general Titles: – TitleFull: A Lightweight Multi-Modal Fusion Framework for In-Vehicle Occupant Detection on Embedded Systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Enqi – PersonEntity: Name: NameFull: Liu, Qi – PersonEntity: Name: NameFull: Pan, Yizhou – PersonEntity: Name: NameFull: Kou, Jiaming – PersonEntity: Name: NameFull: Duan, Hanzuo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 8 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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