Evaluasi YOLOv8 untuk Deteksi Kendaraan pada Simulasi Citra Palang Parkir
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1152Keywords:
YOLOv8; Vehicle Detection; Image Simulation; Parking Barrier; Computer VisionAbstract
This study evaluates the use of YOLOv8 for vehicle detection in an image-based parking barrier simulation. The study was conducted because vehicles near a parking entrance are not always captured under ideal visual conditions. Some vehicles may appear far from the barrier point, partially occluded, captured under low-light conditions, or appear together with other vehicles in a single frame. The data were collected from two sources, namely vehicle photos taken using a mobile phone camera and vehicle images obtained from open internet sources. All images were grouped into five testing scenarios: vehicles in front of the barrier area, vehicles far from the barrier point, partially occluded vehicles, low-light conditions, and crowded areas. The testing process was carried out using the YOLOv8n model with a confidence threshold of 0.5. From a total of 131 test images, the model successfully detected vehicles in 103 images, failed to detect vehicles in 28 images, and produced 0 false detections. The average detection accuracy was 77,6%. The best result was obtained in the crowded area scenario, while the lowest result occurred in the low-light condition scenario. These findings show that YOLOv8n can be used as an initial evaluation for vehicle detection in a parking barrier simulation, although further testing with a live camera and physical devices is still needed. This study contributes to the preliminary evaluation of YOLOv8n for vehicle detection in parking barrier image simulation. The main contribution lies in examining the model’s ability to recognize vehicles under several visual conditions, including vehicles in front of the barrier area, vehicles far from the barrier, partially occluded vehicles, low-light conditions, and crowded areas. This study is not intended to represent a complete implementation of an automatic parking barrier system. Instead, it serves as an image-based preliminary evaluation to identify the potential and limitations of YOLOv8n before further development using live cameras and physical parking barrier devices.
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Copyright (c) 2026 Syalomita Pasha Sante, Claudia Anastasia Danel, Valentino Rexy Artha Sumeru, Olga Engelien Melo, Anthon Arie Kimbal
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