Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident
Electric vehicles (EVs) have played a significant role in sustainability, and EVs fire accidents have raised doubts in recent years. To solve the mobile analytical equipment limitation in EVs fire accident and help staff receive prompt results at on-spot inspection, we provide a lightweight but accu...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Sustainable Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19397038.2024.2369297 |
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| author | Mengqi Lei Xin Wang |
| author_facet | Mengqi Lei Xin Wang |
| author_sort | Mengqi Lei |
| collection | DOAJ |
| description | Electric vehicles (EVs) have played a significant role in sustainability, and EVs fire accidents have raised doubts in recent years. To solve the mobile analytical equipment limitation in EVs fire accident and help staff receive prompt results at on-spot inspection, we provide a lightweight but accurate Transformer that can ideally adapt to the mobile environment. First, we built on the simple Segformer and extended it to aggregate the representations of amorphous objects, such as fire traces, in image recognition. Second, we used shunt-based self-attention (SSA) to enhance the model for capturing multi-scale contextual information and help distinguish the deformed level of EVs after combustion. Third, we redesigned a simple multi-level information aggregation (MIA) decoder to obtain the relationship between pixels in the channel dimensions by a weighted aggregation. Furthermore, to foster image trace recognition, we put forwards and evaluated the accuracy of models on electric vehicle fire traces (EVFTrace), a dataset of images of burnt EVs. On EVFTrace, the mean intersection over union (mIoU) achieves 72.24%. The float point operations (Flops) and parameters (Params) achieve 114.83 G and 89.5 M. Our model shows excellent efficiency and accuracy for burnt EVs segmentation tasks. |
| format | Article |
| id | doaj-art-ad2ea407b5414aabb2dc4c9fb2a06c83 |
| institution | DOAJ |
| issn | 1939-7038 1939-7046 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Engineering |
| spelling | doaj-art-ad2ea407b5414aabb2dc4c9fb2a06c832025-08-20T02:50:19ZengTaylor & Francis GroupInternational Journal of Sustainable Engineering1939-70381939-70462024-12-0117152453610.1080/19397038.2024.2369297Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accidentMengqi Lei0Xin WangSchool of Microelectronics, Tianjin University, Tianjin, ChinaElectric vehicles (EVs) have played a significant role in sustainability, and EVs fire accidents have raised doubts in recent years. To solve the mobile analytical equipment limitation in EVs fire accident and help staff receive prompt results at on-spot inspection, we provide a lightweight but accurate Transformer that can ideally adapt to the mobile environment. First, we built on the simple Segformer and extended it to aggregate the representations of amorphous objects, such as fire traces, in image recognition. Second, we used shunt-based self-attention (SSA) to enhance the model for capturing multi-scale contextual information and help distinguish the deformed level of EVs after combustion. Third, we redesigned a simple multi-level information aggregation (MIA) decoder to obtain the relationship between pixels in the channel dimensions by a weighted aggregation. Furthermore, to foster image trace recognition, we put forwards and evaluated the accuracy of models on electric vehicle fire traces (EVFTrace), a dataset of images of burnt EVs. On EVFTrace, the mean intersection over union (mIoU) achieves 72.24%. The float point operations (Flops) and parameters (Params) achieve 114.83 G and 89.5 M. Our model shows excellent efficiency and accuracy for burnt EVs segmentation tasks.https://www.tandfonline.com/doi/10.1080/19397038.2024.2369297Deep learningsemantic segmentationelectric vehicle firesustainability |
| spellingShingle | Mengqi Lei Xin Wang Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident International Journal of Sustainable Engineering Deep learning semantic segmentation electric vehicle fire sustainability |
| title | Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident |
| title_full | Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident |
| title_fullStr | Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident |
| title_full_unstemmed | Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident |
| title_short | Multi-scale Segformer: image segmentation algorithm for electric vehicles in fire accident |
| title_sort | multi scale segformer image segmentation algorithm for electric vehicles in fire accident |
| topic | Deep learning semantic segmentation electric vehicle fire sustainability |
| url | https://www.tandfonline.com/doi/10.1080/19397038.2024.2369297 |
| work_keys_str_mv | AT mengqilei multiscalesegformerimagesegmentationalgorithmforelectricvehiclesinfireaccident AT xinwang multiscalesegformerimagesegmentationalgorithmforelectricvehiclesinfireaccident |