Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework
Roadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. Thermal imaging overcomes these issues, enabling reliable detection ac...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2340 |
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| author | Arnd Pettirsch Alvaro Garcia-Hernandez |
| author_facet | Arnd Pettirsch Alvaro Garcia-Hernandez |
| author_sort | Arnd Pettirsch |
| collection | DOAJ |
| description | Roadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. Thermal imaging overcomes these issues, enabling reliable detection across all conditions without collecting personal data. However, its widespread use is hindered by the scarcity of diverse, annotated thermal training data, especially since fixed cameras installed at the side of the road produce very similar images with the same backgrounds. This paper presents two key innovations to address these challenges: a novel dataset of 11,400 annotated images and 142 unannotated video clips, the largest and most diverse available for thermal roadside imaging to date, and a weakly supervised incremental learning framework tailored for thermal roadside imagery. The dataset supports the development of self-supervised algorithms, and the learning framework allows efficient adaptation to new camera viewpoints and diverse environmental conditions without additional labelling. Together, these contributions enable cost-effective and reliable thermal-based traffic monitoring across varied locations, achieving an 8.9-point increase in mean average precision for previously unseen viewpoints. |
| format | Article |
| id | doaj-art-7d34dcff476344c9866f979f2f5e3f27 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7d34dcff476344c9866f979f2f5e3f272025-08-20T03:08:56ZengMDPI AGSensors1424-82202025-04-01257234010.3390/s25072340Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning FrameworkArnd Pettirsch0Alvaro Garcia-Hernandez1Institute for Highway Engineering, RWTH Aachen University, 52062 Aachen, GermanyInstitute for Highway Engineering, RWTH Aachen University, 52062 Aachen, GermanyRoadside camera systems are commonly used for traffic data collection, yet conventional optical systems are limited by poor performance in varying weather and light conditions and are often restricted by data privacy regulations. Thermal imaging overcomes these issues, enabling reliable detection across all conditions without collecting personal data. However, its widespread use is hindered by the scarcity of diverse, annotated thermal training data, especially since fixed cameras installed at the side of the road produce very similar images with the same backgrounds. This paper presents two key innovations to address these challenges: a novel dataset of 11,400 annotated images and 142 unannotated video clips, the largest and most diverse available for thermal roadside imaging to date, and a weakly supervised incremental learning framework tailored for thermal roadside imagery. The dataset supports the development of self-supervised algorithms, and the learning framework allows efficient adaptation to new camera viewpoints and diverse environmental conditions without additional labelling. Together, these contributions enable cost-effective and reliable thermal-based traffic monitoring across varied locations, achieving an 8.9-point increase in mean average precision for previously unseen viewpoints.https://www.mdpi.com/1424-8220/25/7/2340thermal imagerytraffic data collectionroadside camerasweakly supervised learningincremental learningthermal image dataset |
| spellingShingle | Arnd Pettirsch Alvaro Garcia-Hernandez Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework Sensors thermal imagery traffic data collection roadside cameras weakly supervised learning incremental learning thermal image dataset |
| title | Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework |
| title_full | Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework |
| title_fullStr | Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework |
| title_full_unstemmed | Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework |
| title_short | Overcoming Data Scarcity in Roadside Thermal Imagery: A New Dataset and Weakly Supervised Incremental Learning Framework |
| title_sort | overcoming data scarcity in roadside thermal imagery a new dataset and weakly supervised incremental learning framework |
| topic | thermal imagery traffic data collection roadside cameras weakly supervised learning incremental learning thermal image dataset |
| url | https://www.mdpi.com/1424-8220/25/7/2340 |
| work_keys_str_mv | AT arndpettirsch overcomingdatascarcityinroadsidethermalimageryanewdatasetandweaklysupervisedincrementallearningframework AT alvarogarciahernandez overcomingdatascarcityinroadsidethermalimageryanewdatasetandweaklysupervisedincrementallearningframework |