Geometry and Color Transformation Data Augmentation for YOLOV8 in Beverage Waste Detection

Sabar Muhamad Itikap, Muhammad Syahid Abdurrahman, Eddy Bambang Soewono, Trisna Gelar

Abstract


In the bottle sorting process in real world, there are some beverage packaging waste that is deformed. Deformed objects can result in detection errors by an object detection system. Detection errors can also occur in attributes that share similar feature maps. Detection errors can be caused by models that are unable to generalize to the data. Several methods have been devised to prevent such issues, with data augmentation being one of them. To increase the variety of data, data enhancement techniques will be utilized. This research employs a data augmentation technique that concentrates on geometry transformations such as scaling and rotation, as well as color transformations such as hue, saturation, and brightness. Additionally, a combination of geometry and color transformations was conducted, resulting in a total of 39 experimental scenarios. This study demonstrates that data augmentation can affect the model's performance in terms of accuracy and the number of detection results. The combined method of scaling and rotation, which is applied to the original data, reveals the optimal experimental scenario with an accuracy of 88.4%.

Keywords


Data Augmentation; Detection; Data Variation; Scaling

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DOI: https://doi.org/10.17509/seict.v4i2.64400

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