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Construction Vehicle Activity Detection in Low-Frequency Surveillance Imagery and Its Relationship to Local Air Quality
Ploesteanu, Dan-Cristian (2025) Construction Vehicle Activity Detection in Low-Frequency Surveillance Imagery and Its Relationship to Local Air Quality.
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| Abstract: | Construction vehicles are both central to construction site workflows and major contributors to local air pollution. This paper develops a 3-stage machine learning pipeline that uses sparse on-site surveillance imagery to detect and classify construction vehicle activity and quantify its relationship to ambient air quality. The pipeline comprises a detection model based on the YOLOv9 architecture, a construction vehicle activity classification model (for which two contrasting architectures are tested, including a ViT-based method and an SVM-based model) and a linear regression analysis between detected vehicle activities and local air quality indicators. Despite operating on low-frequency (5-minute interval) imagery under real-world conditions, the proposed models achieve state-of-the-art performance in both detection and activity inference. Regression analysis reveals a statistically significant but limited correlation between vehicle activity and local pollutant concentrations, suggesting the presence of dominant external sources. These findings demonstrate the feasibility of passive, vision-based environmental sensing in constrained urban deployments and open new avenues for integrating ubiquitous computing with sustainable construction monitoring. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Awards: | Best Paper Award |
| Link to this item: | https://purl.utwente.nl/essays/107330 |
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