Abstract
If water trash exceeds the allowable load of a trash barrier, water trash barriers could be destroyed and the spilled waste negatively impacts the environment. Therefore, it is essential to measure the trash load in the water infrastructure to process collected water trash in a timely manner. However, there has been little investigation about how to monitor water trash in an automated way. To fill the knowledge gap, this study presents detailed investigation of water trash monitoring methods based on object detection models. To verify effective detection models and their performances, a new dataset is established, called the Foresys marine debris dataset. The dataset consists of a total of 6 water trash categories (Plastic, Vinyl, Styrofoam, Paper, Bottle, and Wood). State-of-the-art detection models were employed to test their performance, such as YOLOv3, YOLOv5, and YOLOv7 pretrained on the COCO dataset. The experiments showed that the detection models could achieve decent performance with proper amount of training image data; a number of training data required to secure decent performance varies by target class. The findings of this study will give a fresh insight for developing an automated water trash management system.
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Acknowledgements
This research was conducted with the support of the “2022 Yonsei University Future-Leading Research Initiative (No. 2022-22-0102)” and the “National R&D Project for Smart Construction Technology (No. 22SMIP-A158708-03)” funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.
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Kim, S., Kim, T., Hyeon, J., Won, J., Kim, H. (2024). Comparing Object Detection Models for Water Trash Monitoring. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_13
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