Optimizing YOLOv11 for Accurate Car Parts Segmentation in Automotive Industry Applications

FX Hendra Prasetya

doi:10.24167/sisforma.v13i1.15499

Optimizing YOLOv11 for Accurate Car Parts Segmentation in Automotive Industry Applications

FX Hendra Prasetya

Abstract

We study the application of the YOLOv11 (You Only Look Once version 11) model for automotive component segmentation. Computer vision technologies are becoming increasingly important to the automobile industry. They help to improve manufacturing, improve quality control and make inventory management easy. To automate these operations, it is required to be able to segment automotive parts accurately. This means you can correctly identify and sort out parts in images. The proposed study uses a dataset of different images of car parts with annotations for training the YOLOv11 model. The research aims to improve the architecture and training parameters of the model to achieve high accuracy and efficiency in the segmentation of various automotive parts including engine, wheels and body panels. We can see how well the model works using performance metrics like Intersection over Union (IoU), precision, recall and F1-score. Expected results of this study are a powerful segmentation model which can be integrated into existing automotive systems to improve operational efficiency and reduce human error in recognition of car parts.

Keywords

YOLOv11; Car Part Segmentation; Semantic Segmentation; Automotive Industry; Deep Learning; Computer Vision

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References

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DOI: https://doi.org/10.24167/sisforma.v13i1.15499