Implementation of YOLOv8 Algorithm for Car Seat Detection in Automotive Industry with GUI

In automobile production, precise car seat position detection is critical to prevent mistaking labels and to improve the production efficiency. In this paper, an improved car seat detection system using YOLOv8 object detection algorithm combined with a real time Graphical User Interface (GUI) and SQLite database is proposed. Extending from earlier models based on YOLOv5, the models perform better accuracy and add bar code validation automatically to car seat type A, B, and C. The dataset contains 1,406 labelled images, which is trained by Roboflow and later on Google Colab. Built on CustomTkinter, the GUI supports real -time detection and database retrieval. After evaluation, the system achieved an average accuracy of 87%, 84%, and 89% in seat types A, B, and C, respectively. The results reveal the superiority of the proposed system on the detection accuracy, reduces human labelling errors, and accelerates verification process on automotive production lines.