Abstract
In this study, we provide theoretical and practical studies required for detecting cigarette butt with machine learning techniques under the framework of YOLO. To achieve our goal, an image dataset was created manually by taking images in real life for different angles, slopes, and distances. The necessity of these images to be in different places at different times is experienced in the study since cigarette waste could be anywhere in public, agricultural areas. The image dataset consists of 2100 images at 1000 × 800 resolution in total for train, validation and test. Every image has been labeled manually for the study. According to our experiments, we obtained promising results (0.889 mean average precision) on our dataset.
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Yazici, H., Danişman, T. (2023). Machine Learning Based Cigarette Butt Detection Using YOLO Framework. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_50
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DOI: https://doi.org/10.1007/978-3-031-09753-9_50
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